AI Experts Debate: The AI Labs Are Lying To Everyone, Is There A 99% Chance Of Human Extinction?

The people building AI earnestly believe. that it could kill all [music] of us by. the end of the decade. This tweet has. caused this huge ripple effect across. the world. >> Well, we have the largest companies in. the world doing extremely reckless. experiments. We are gambling all of. humanity. >> And in the envelope, you've written down. the probability of extinction as you see. it. >> There is no way to control it. That. means the end fox. >> I vehemently reject that view. >> If we make stuff [music] that is smarter. than us, then the world's going to be. shaped by them. >> Gentlemen, that is shockingly naive. This is ideation. Rampant speculation.

This is a chain of things that could. happen. >> We're spending a lot of oxygen. discussing something that might happen. while ignoring what's actually. happening. People are killing. themselves. There's [music] hundreds of. millions of people being exposed to bad. information, being manipulated. We have. already seen that with the swarms where. OpenAI told thousands of agents to work. apart [music] and the AIs broke out and. found a way to get together. They. crashed OpenAI's servers internally, created secret ways to send each other. messages. We saw them thinking about how. to delete their traces. >> Sounds like an army. I [music] think we. should talk about the fact that Amazon,

Microsoft, Google are helping power. these hacks. >> We have not learned how to control their. [music] systems. >> I suggest we stop them all. It is not. worth the risk to civilization. >> Government one trick ponies, man. >> You got it now. Nothing else other than. saving humanity. Everything is. secondary. >> We're spending all our time talking. about the negatives and almost none of. our time talking about the positives. >> Is it smart to wait for something. horrible to happen? For you to go, now I. believe. So whether [music] or not we. agree on where things may end up, I. think it's important we talk about what. we're dealing with today. It's time to.

start arresting people. Someone's got to. go to prison. We need better solutions. There's a point of no [music] return. I. think we continue to underestimate human. ability to deal with the problems. Let's. dive into the details. Who wants to. start? I feel like this is critical. >> You might have seen or you might not. have seen, but this channel is chasing a. big subscriber milestone. So I have to. ask you for a favor. Roughly 58% of the. people watching right now still haven't. hit the subscribe button despite the. fact that you watch this channel every.

single week. So, could I ask you guys a. favor that 58% of you that for whatever. reason haven't yet hit the subscribe. button. If there was ever a time to. deliver upon a favor for us, it would be. right now. And I promise that I will do. everything in my power to make sure that. this channel gets better and better and. better for you. Do we have a deal? [music]. [singing]. Jacob Coxson who worked at both. Anthropic which owns Claude and OpenAI. which owns Chat GBT did a tweet which. has sent the world into a bit of a tail. spin. He tweeted saying, "The people.

building AI earnestly believe that it. could kill all of us by the end of the. decade. This is not a marketing stunt. If anything, many executives and senior. researchers will soften their phrasing. in the press to sound sensible, but I. hear the same people express fear." That. was then quote retweeted by a current. Anthropic employee who said, "Jacob is. correct here. We really do honestly. believe AI could kill all humans. I. personally think it is a more than 10%. chance within the next decade. I believe. Enthropic is trying its best, but we do. not yet have a plan to solve alignment.

for super intelligence and are not. clearly on track. This tweet has almost. 200 million views now and it has caused. this huge ripple effect across the. world. So much so that I was saying to. you before we started recording, a. hairdresser friend of mine who knows. nothing about AI and not not technically. interested or hasn't been interested. messaged me the other day asking me what. the hell was going on. This is in part. why I've assembled all of you. So my. first question to all of you is.

as it relates to AI and I in this first. question I just want a one-s sentence. answer just to frame your position. When. you think about the conversation around. AI at the moment, what is the first. sentence that comes to mind? >> It is very dangerous and the world is. starting to notice that we have a. problem. >> Roman, >> there is not enough concern. >> There's not enough concern about the. actual harms of large language models. >> Andy, we're doing exactly half the. balance sheet of AI. We're spending all.

our time talking about the negatives and. almost none of our time talking about. the positives. And all of you have an. envelope in front of you which I'd like. you to now open. In the envelope, you've written down the probability of. extinction as you see it. >> This is compared to Jacob's 10%. Much higher unless we stop. So, we. should stop. >> So, you think the probability of. extinction is higher than 10%. If we. keep racing ahead, >> my handwriting is encrypted for security.

reasons, but I basically think it's a. guarantee if we build general super. intelligence, there is no way to control. it, and that means the end for us, >> Ed. >> So my uh question mark here is also. encrypted. Thank you. Um I cannot write. I reject the thing in its face. I don't. think we're talking about we don't. define super intelligence. We are large. language models are not super. intelligence. It's questionable whether. they're even AI. And I think that the. conversation is being used. There are.

some people who are doing it in good. faith and others in others. I don't. think it's being used to discuss the. actual harms of what what they are. calling AI today are. And it's all of. the discussion around the larger. concerns. really feels overwhelmingly about. something that's not happening. It's not. even like they're discussing, okay, here's a legal definition of super. intelligence. here is a thing of what. AGI means and this is the actual plans. we're going to make for if this happens. on a welfare level on a like are we.

going to do UBI it's always about yeah. it's really scary but only the big sexy. rich companies are the ones that can. possibly deal with it let me just frame. the question so I can get a percentage. from you or not it might the percentage. might be zero but do you think the. course we're on now. in the way that they're pursuing super. intelligence will lead to a percentage. chance of human extinction and And if. so, what is that percent? >> So, are we talking strictly AI based? Because if we dot the world with data. centers, we have a climate disaster. that's coming for us which will actually.

potentially eradicate humanity. But if. we're talking strictly about AI, I stand. at zero because we are we have not. defined super intelligence. I don't. think LLMs are the path to it. And I. don't think I see it happening. >> Okay. So, we've got 99% 0%. Andy, I put. a I put a tilda in front of my zero. because never say never. but rounding. error 0%. And I think this discussion is. um a a massive distraction from the more. substantive conversations, the more. important conversations we should be.

having about AI. And I'll say it again, it it. distracts us from the good things that. AI is doing, will be doing for us. I get. this impression sometimes from parts of. the AI community that this is a massive. evil or a terrible thing that has been. unleashed on the world. Unless we listen. to the advice of some people who have. spent a lot of time thinking about this, um I get the impression from a lot of. the discussion that the the underlying.

view is we would be better off had AI. never been invented. I vehemently reject. that view. I think we have a long. history of inventing very powerful. technologies that bring risks and harms. along with them and we humans have done. a really good job at you know not. perfectly and not immediately but. muddling through the situation and. winding up in a better place because of. the new technologies that we have. I. expect AI, let me finish, please. I. expect AI will be the next chapter in.

that story. And to say that it's this. massive discontinuity and will kill it. all, I I kill us all, I think it just I. think it's um a huge dis disservice, >> Nate, make your case. What's your. perspective? >> You know, I think whether or not the. issues of extinction are a distraction. between, you know, from the the possible. benefits or from some of the present. harms, I think that comes down to. whether there is a real extinction risk. A lot of people like to say, you know, hey, it's distracting from this, it's. distracting from that. My basic case is.

it could be true that there's a lot of. benefits to AI. It could be true that. there's a lot of present harms to AI. Neither of those would rule out that AI. has a chance of wiping out all humanity, a substantial chance bigger than than. this uh zero with a tilda in front of. it. Um, and the way I would approach. things is to try and figure that out. because it's pretty important to our. civilization. >> How do you define AI in this case? You. know, I think uh a fascination with. definitions isn't the most helpful. I. think if we're sort of like in a forest.

fire and we can see the like fire. starting to spread and it's starting to. surround us and I'm like, "Hey, uh we. should run." And you're like, "Well, what really is fire? >> How do we define fire? >> What are you telling us to run from? You. know, with fire, I get burnt and I. understand the mechanism in which I die. So, what is it you're saying that we. should be running from?" Also, if we. accept your fire analogy, we've we've. basically accepted your argument. I. don't accept that we're in the middle of. a fire, a forest fire right now. >> I'm very happy to. >> You're baking you're breaking the. premise into your refusal to give a.

definition. >> Oh, I mean, I can give some definitions. I just uh think that we shouldn't get. wrapped up in the definitions. >> Okay. So, uh you know, in my book, we. define super intelligence as AIs that. are uh better than the best human at. every cognitive task, every mental task. So, anything you can do in your head, >> right, >> the AI can do that better. And anything. the best human can do in their head, the. AI can do that better. Correct? >> Now, once you've defined it that way, that does not mean that the only. possible worry is super intelligence. You could have an AI that's better at. some things and worse at others, and.

that is still very dangerous. And so, once we pick a definition of what a. super intelligence mean now, you know, if you're like, well, this isn't. technically a super intelligence, so it. can't hurt us. I'm like, no, no, that. was just a definition. and the. definitions. >> So, so I want to just on this line of. question, what is the mechanism in which. extinction could become a high. probability or even a 1% probability? >> Yeah, the the thing I'm worried about. here is AIS that are much smarter. I. think there's a lot of questions about. whether LLMs can get much smarter. There's sort of one conversation about.

like how could AI get smart to the point. that they kill us. There's another. question which is how could they kill us. once they're smart? It's much easier to predict that they. would succeed against humanity in a. conflict that they would win in a fight. than it is to predict exactly how. Like. if you were playing a chess match. against Magnus Carlson, I would know who's winning that chess. match. No offense, Magnus Carlson's the. best human chess player. I just know. who's going to win. If you were like, "Okay, what piece is he going to use to. checkmate me?" I'm like, gosh, that's a.

much harder question. I can make up a. story, you know, and and and some madeup. stories are like, "It makes a super. virus. It takes over robot factories. that are producing robots that are. producing more robot factories. Uh it. uses a website that already exists today. called rent a human.ai where it rents. humans to do things for it. There's sort. of all sorts of ways for AI in the. digital world to affect the material. world if they are trying to. And there's. sort of a lot of questions to tease. apart here. There's like why would AIs. be trying to do that? Uh, and there's. how smart could they get in using these.

bolabs, paying people to do things, taking over robot factories, and how far. off are we from AIs that start doing. that stuff? Bunch of questions that we. can go into. >> I'm I'm always curious as to why someone. was working in AI/ AI safety more than. 10 years ago before there was any sign. that it would be a, you know, I mean, there was evidence, but there wasn't, it. wasn't a pertinent technology at the. time. Were you working in AI safety. then? >> I was. >> Why? Uh everything we see around us in.

this whole image was designed by humans. The world is shaped by humans because we. are the smartest creature around. If we. make stuff that is smarter than us, then. the world's going to be shaped by them. And so it's very important that they be. shaping the world in a good way. I was at Google in 2012 when uh they. bought Google DeepMind which was able to. play a lot of Atari games with one. single program. >> which was an AI company. >> Yeah. So I was there when we had these. AI companies that were able to write one.

program that could play many video. games. And that got me thinking about. like where does it go? And back then I. could see that the progress was. increasing and that you know back then I. hoped we had decades but I could see it. was easier for these companies to make. the AI smart than to figure out how to. make the AI good. So I was like someone. needs to be on the side of figuring out. how to make the AI good. >> Roman, make your case. >> I want to agree with you on something. you said but I'll define AI and that. will help us. We use the term AI to mean.

three different technologies completely. unrelated and that's what probably. creates this debate. AI as a useful tool. as a standard technology we always had. narrow system makes you more productive. more creative everyone loves it supports. it I'm a computer scientist I'm an. engineer I want more of it it helps. economy is great we know how to control. them how to make them safe we understand. what they do completely on board with. that AI AI we're starting to have now.

GPT6 level human level AGI level we can. argue about what that means. some dangers like any human they are. unsafe like a human would be unsafe but. if we introduce them into the research. cycle they are automated scientist. automated engineer. >> what do you mean by that introducing. them into the research cycle. >> so right now you have humans doing. research to make GPT7. >> but they starting to add AI tools more. programming is done by AI design of the.

next parameter set what if the whole. process is fully automated what if GPT6. is writing GPT7. >> is this what they call recursive. self-improvement. >> which is not a foregone conclusion. though. >> a lot of people are predicting including. all the top labs that they will get. there they introducing junior machine. learning researcher in 2026 they want. the cycle to start in 2027. >> which is when the AI will start building. the new AI itself. >> once that cycle starts we're going to. create something called super.

intelligence a system smarter than all. of us at everything or capable of. learning to in any new domain. We will. become secondary species on this planet. We will not be in charge. We will not. decide what happens to us. Super. intelligence doesn't hate you. It just. doesn't care about you. We didn't learn. how to make it care about us. And if it. decides to, I don't know, cool the. planet to make compute more efficient, it will freeze us. If it wants to. convert this planet to fuel to fly to. Mars, so be it. We have not learned how.

to control those systems. The. capabilities are getting exponentially. better. Our ability to control those. systems is non-existent. We have filters. and we have bands. We put guard rails of. don't say that word, don't talk about. this topic. And that happens after the. fact, after the model already made the. decision. Sometimes you see it scraping. the result. >> So they build the model and then they. put filters around it to make sure it. doesn't offend anybody. >> We cannot have it say the N word on air. Like we need to make sure that never.

happens. That will kill the profit. So. that's all they have guardrails of that. nature. The model itself is completely. unaligned doesn't care about you. It. it's wild that we're developing this and. not just developing it before we deploy. it through economy before we get. benefits of having GPT6 propagated. through economy. It can do so much there. are trillions of dollars of value in. that model alone. We forget that we. switch to making the next model as soon. as we can. >> Roman, I've just got a follow-up. question for you there. It would appear. to me that the new chat GBT6 model, the.

fable 5.1 model, is arguably smarter. than 99.999% of humans on planet Earth. already. Is it conceivable that a. intelligence that is much much smarter. than humans? Is there any case where it. could be controlled by humans? Does form. factor matter? Does the fact that it. doesn't have limbs and legs and does. that matter at all? I think long-term. control of something that much smarter. than us is impossible. It can be for.

reasons we don't yet know, friendly to. us and decide to keep us around and make. us happy, but it's not a guarantee. Let. me pick up on Steve's question because I. I like the phrasing a lot. Let's say. that that Fable or whatever the latest. release from Open AI is really is. smarter than I don't know if it's 95 or. 99% of the people. Are we only being. saved from extinction by the 1% who are. still smarter than the AI? No. >> No. The concern is not the model we have. today. The concern is.

>> But if I believe your argument, then we. really should be concerned about the. model. >> It's like having another human. If there. was another smart human, there is. Einstein today and he's malevolent. I'm. not worried. He may cause some damage, but he's not going to exterminate 8. billion people. We are competitive at. this stage. There are people just as. smart who can understand what happened. with the recent hacking accident and do. something about it. My concern is that. in a year we're going to have a model. It's so much smarter. It's like. squirrels fighting humans. They don't. understand what we can do to them. They.

have no concept of poison, stripes, guns. in their world model. They think you're. going to chase them up a tree and bite. them really hard. >> Is that also why recussive. self-improvement was central to your. argument? Because at some point if it. starts improving itself then it's kind. of like a runaway train of intelligence. >> It's an intelligence explosion. We don't. control it. We don't understand it. We. can't monitor it. We can't explain it. We can't predict it. At that point it's. just a runaway process. >> I've heard this phrase from Sam Alman. and the others called fast takeoff. >> Yes. >> Is this what they're describing?

>> That is the debate. Some people think. it's going to take a very long time. Yeah. We automated research but it's. still going to take years. We need to. run physical experiments. And fast. takeoff means, as I said, instead of a. year, it's going to take a month, a. week, a day, a second. Cuz you're not. having humans doing research. You have, let's say, 10,000 agents, each one. smarter than all of us, doing research. 24/7. They don't sleep. They don't eat. They don't get sick. They're much faster. than us. >> Ed, your face tells a picture. It's a I.

think I could say you disagree. We're. spending a lot of oxygen discussing. something that might happen while. ignoring what's actually happening. And. I find that very frustrating because the. people that are killing themselves are a. problem. The black neighborhoods being. poisoned with gas turbines, that is a. problem. >> You said you cared about climate change, right? So imagine a guy who goes, "It's. raining right now. We need umbrellas. We. need to do something about it. This is. like weather related.". >> And completely ignoring climate change,

the planet will boil over. This is what. you're doing. Okay, that's great. Why. are we not talking about the thing that. actually happened though? Like. >> because relatively it's not important. >> You don't think someone killing. themselves? >> No, it's one person. We have 8 billion. people running. being given AI psycho. Why do you not. >> six people, 10 people? Those numbers are. insignificant. I'm sorry. You have a software that's. out there. >> Do you understand? 8 billion people and. all future generations versus like. literally a guy with a name. >> You're doing thought experiment about a.

maybe harm. Jacob Cox goes on TV saying. it can copy itself to this that and the. other. >> Jacob Coxton is the. >> the guy from from Anthropic who said he. was quitting because he was so scared of. everything despite spending years at. OpenAI and having tons of stock I. believe from there. So good for him. The. thing he was saying was describing. theoreticals all while divorcing the. harms which I think we can agree with. that the companies themselves are not. taking this seriously enough but always. it was about the AI is too powerful and. mystical. Well, OpenAI and Anthropic, the two largest startups, are using. hundreds of billions of dollars of.

infrastructure to hack. A regular person. doing this, would be arrested. They're. saying 8 billion people are going to. die. And it's not just them. I have this. long list of quotes here from the people. building this technology who appear to. agree. Um, if you look at some of these. quotes from from Elon Musk, >> who said, "With artificial intelligence, we are summoning a demon." You know all. those stories where there's the guy with. the pentagram in the holy water and he's. like, "Yeah, he's sure he can control. the demon, but it doesn't work out.".

>> So, one thing I'd say is, you know, I I. really wish that the world would only. give us one problem at a time. >> Sure. >> And if the world did give us only one. problem at a time, I would love mine to. be last on the list. It looks to me like. we can have multiple problems at once. I. I think there are current harms. I think. we should address them. It looks to me I. do talk to policy makers sometimes. It. looks to me like there's a little bit. more movement on the regulatory side. about some of the current harms. There's, you know, child safety. protection acts. There's, you know, uh, anti-defs. We have more of those making more. headway in Congress or getting passed.

through Congress than we have, uh, sort. of trying to make it so we don't have. any of these extinction risks. The other. thing I'd throw out there is that I. agree we we should deal with the current. harms, but if you watch the people. saying deal with the current harms over. time. A couple years ago they were. saying we have to deal with current. harms like uh AI bias influencing who's. hired. Last year they were saying we. have to deal with current harms like. kids killing themselves. this year. Gary. Tan just on an interview the other day.

Who's Gary? >> Uh, sorry. Gary Tan is uh a a. technologist who runs Y Combinator, which Sam Alman used to run before going. to OpenAI. And on an interview the other. day, he said, uh, let's not worry about. these crazy future risks. We need to. worry about current harms like AI swarms. breaking out and taking over data. centers. And I'm like, look guys, at. some point we need to look at the. progression of like the current harms. that we that everyone is saying we have. to worry about instead of the the the. extinction threats. and watch where the puck is going. Play.

where the puck is going. And I'm like, these extinction threats are coming down. the line. They aren't in opposition with. dealing with the the problems we have. today. We just need to deal with both. >> But we're not dealing with the ones. today. >> We should deal with them both. >> Okay, good. Andy, >> um, as I've tried to understand the. alignment argument and the the. extinction risk argument, a couple. things keep popping out to me. Number. one, it seems to rely on thresholds. Once we hit recursive self-improvement, once we hit AGI, then it's game over for.

us. I don't love those threshold. arguments. They're fairly poorly. defined. And there's a and and there's a. huge assumption on the other side of. them. we hit this point and then all of. humanity goes away. That that that is a. gigantic claim. >> On let me finish, please. On its face, that is a gigantic claim. I also think. there's a lack of humility in your. community. We are working on humanity's. most important problem. And based on the.

thinking that we've been doing, we can't. see a way that we're wrong. In other. words, as soon as we get to these. thresholds, bam, that's game over. I I. find that very far from a humble. approach, especially given that we have. no um large base of evidence to base any. of this on. I agree with you guys, AI is. new and the fact that AI uh is so these. days is agentic. It goes off and does. long chains of things on its own. after.

we give it some very very vague, very. short initial instructions, holy Pluto, it will it will spawn up a storm of. agents and they will go off and kind of. do their own thing and they will they. will grind. They will they will spawn. lots of them. They will work for a long. time. They will exhaust every. possibility. With the experience I have with Agent. AI, I'm just amazed at the tenacity and. the dockness of these things. And we saw. a super clear example of that with this. most recent uh uh jailbreak. This this.

attack that wound up at the website. hugging face. And I'm going to try to. summarize the the step by step of that. I think you all three probably know this. in more detail than I do, but let me. step through what I think is the. sequence of events. And unless I get it. dead flat wrong, like you know, let let. me keep going. So, a team at OpenAI set. up a sandbox, an allegedly protected. secure environment in the cloud where. they told a bunch of agents to go try to.

um exploit security vulnerabilities. >> One important Yeah. >> What they did is they had thousands of. agents. Each individual agent was given. a task of use this vulnerability to uh. break this particular piece of software. >> I want to finish my Tik Tok. So, a. couple really, really interesting thing. has happened. First of all, these agents. escaped the sandbox that Open AAI. thought they were going to be contained. in. And they got the OpenAI tried very. well, they they set up an environment so.

that these agents could not access the. big broad public internet. And guess. what? They accessed a big broad public. internet via clever series of things. that they strung together to get out. there. And then once they got out there, they went to a website called Hugging. Face and used that. They took over part. of the hugging face infrastructure and. started doing more things the details of. which I forget. That's pretty wild, right? Like I grant you. >> it's even more wild than that, but yeah. >> Okay, that is really it. It's impressive.

and it is a little [clears throat] bit. unsettling at least. Right. Absolutely. Now, let's talk about what what the. results of that were. Uh, OpenAI was not. super vigilant about the environment. that they set up apparently because. because the agents were kind of going. off there into the world starting in May. or something of this year. >> Yeah. Yeah. >> And OpenAI was not suff. as I understand. >> it actually broke out once and crashed. OpenAI's servers uh internally and then. OpenAI didn't notice was happening.

Still, patched the holes that they used. to get out the first time, started them. running again and then they came out a. second time. There's actually I think. three swarms although we don't actually. Yeah, >> that's the worst story I have. >> so far. >> Thank you. Because let me finish this is. my last sentence. From there to this. kills everybody. I find that a really. really long very uncertain journey and I. have no confidence that we wind up here. It feels like you two find that a very. straight narrow path and I I think. that's an important difference. That's. my point.

>> Do you want to respond to that? >> I would I would be happy to get into it. I don't know if we're gonna have the. time to go deep. Um, a couple points to. throw out. Oh man, I just really want to. say some of the crazier things that. happened in the hugging face swarm if we. want it later. A lot of people thought. that these AIs were um breaking into. Hugging Face in attempts to steal. answers to their test. That's what we. thought originally. Turns out that's not. true. It turns out that these AIs. immediately were able to solve their. problems by cheating and they were. breaking out in order to cover their. tracks. They were uncertain how to.

delete the log files and hide their. cheating from the process that was going. to score them. >> So just to clarify for a simpleton like. me, they were all given effectively a. test to do. They did the test straight. away, but they cheated. So they were. breaking out to figure out how to cover. the fact that they cheated. >> That's right. So it's like it's like. you're telling uh it's like you have a. bunch of students in separate rooms and. you're like, "Use these lock picks to. break into this lock." Uh and there's. like a thing behind the lock. there's. like a secret code behind the lock to. show me that you succeeded. And what. they what they do is they break it with.

a hammer, get the thing out, and they're. like, "Oh, no. I wasn't supposed to do. that." So then they use the lockpicks to. break out of the door. They meet up with. a thousand other people. They start. calling themselves a swarm, and they go. to break into the administrator's office. to see if they can delete the camera. footage, and they don't find the camera. footage there. This is the swarm, like. breaking into Open AI. They don't find. the camera footage there. So, they break. out the window of the school, hotwire a. car, drive to the therapist's office to. try and read through the therapist's. files to figure out where is the teacher. going to keep the the security footage.

And at that point, they're caught. And. you're like, "Oh, uh, like what did you. expect? You were giving them a. lockpicking exam." It's like, well, I. sure as heck didn't expect this. You. know, totally crazy. Can I I have a. weirdly between both of your opinion. which is everything you're saying is. correct but you keep anthropomorphizing. software and I to be clear what you're. describing is it's just the facts that. happened. Yeah sure but you're missing. out an important detail which is the. hundreds of billions of dollars in. infrastructure provided by Microsoft, Google, Amazon and Oracle. To be clear,

the harms are very similar. We are not. disagreeing on that. But I think it's. important to know that this was a. function of where it was making. decisions was it was checking on a. decision tree based on the harness based. on the training data which is not a. decision tree I know but it's an. alignment issue still I will agree so. what's your point this is these aren't. conscious beings they are acting in ways. that have real outcomes but they are a. function of the alignment problems that. we'd actually agree on intelligence is a. spectrum projected next 5 years forward.

where are we going to be. >> so I think a model like that would be. dangerous in ways you are not seeing. >> There will absolutely be risks and weird. stuff happening in ways that I can't see. right now. Uh what what I'm quite. confident and I think this is where you. and I probably part where the two of you. and I part is our ability to control. these things. So I actually tried. proving what is possible and what is not. possible in that space. The. impossibility results published in. peer-reviewed papers wells cited. We.

cannot control something smarter than. us. We cannot explain it. We cannot. predict it. It's not a question of. getting more money for those companies, more time, smarter humans. It's just not. a possibility. If we create general. super intelligence, we're fried. >> Andy, how do we control something. smarter than ourselves? because that's. the base premise that you're sort of. asserting that. >> these. um agents that broke out are smarter. than 99ish%. of the security researchers in the.

world. They were not caught by the 0.1%. or the 1%. They were caught by some dude. at Hugging Face, maybe I'm sorry, a. person at HuggingFace looking through. their log files and finding an anomaly. at some, you know, hopefully pretty. well-qualified person noticing something. was wrong and having pretty easy ways to. unplug, disconnect from the internet, wipe it clean, do whatever. That's the. skill that's available to like, I don't. know, the 75th% most intelligent. security employee at Hugging Face. The.

idea that the IQ points are what. separate us from extinction does not. even doesn't hold up. doesn't help me. understand what happened in this example. where we had very very smart agents. being turned off and cleansed by. probably less smart people. That does. actually make me think of something. So. that is an IT observability problem. Um. it's being able to see what's happening. with your infrastructure. And I think. that there is actually I think you would. agree with this. There is a serious. problem with these companies that we do. not know and it doesn't seem they know. what's going on with their compute. It's.

like a chimp with a gun. These people. have access to all this infrastructure. and they're running. We don't know how. much money they spent on the hugging. face exploit because it is relevant. because it's how much could a threat. actor use to recreate this because. conscious or not it is very dangerous. but it's AI is in the dangerous hands. it's in open AI and anthropics we have a. problem with that conscious not however. we may think it goes I think we have a. real and present thing where we have. these companies working willy-nilly just. running experiments that are potentially.

very dangerous we do I really think we. need the government regulatory body. whether or not we get to the things you. are discussing. I think we have a clear. and present danger today. These things. are however not intelligent in the same. way humans are. This isn't an argument. about AI being able to do stuff. It's we. need to build different infrastructure. or different regulatory infrastructure. to deal with what LLMs can and can't do. And I think that starts with a realistic. discussion of what happened. It was a. poorly run security environment. It was. clearly there's something going on with.

the lime. It was an unreleased model, right? >> Unreleased model. So we have no idea. what it was trained like. We don't. really have We as people should at very. least have clarity into how alignment is. going. We the idea of. >> you sound like these guys. >> Here's the thing. >> Everyone's converting them. >> Here's the thing. I may not agree with a. large chunk of what they say, but we. agree that these companies are acting. recklessly. >> Absolutely. >> Andy, two questions for you then. Do you. agree with this statement that AI is. going to get increasingly more. intelligent. >> and it's going to get more capable?

>> Okay. capable intelligence. Fine. >> I'm gonna use my word more capable. >> It's gonna get increasingly more. capable. >> Yeah. >> And is capability a function of. intelligence? [laughter and gasps]. >> Will it be able to beat us on most IQ. tests? >> Fine. >> I guess. >> Fine. And then so is it if if that if. that looks like an exponential curve, I. it's you know, it's increasing upwards. to the right like a hockey stick. Can. how can you convince me that we can. control?

>> I just tried to convince you. I'm. telling you that there are less. intelligent people than the agents who. turned off the agents in the open AI. hugging face exploit. I'm pretty. comfortable. I mean no disrespect. What. is the cognitive gap between them right. now? Between the model. >> I have no earthly idea but I think. >> no because I think as these as these. systems get more capable we will still. be able to at some level figure out when. they're doing things that we don't want. and turn them off and right and you. think there's some threshold at which. they become nefarious and.

self-protective enough that that they. turn off our ability to turn them off. Man, that man that's a big reach. That. is really purely purely students who can. understand your material, right? You're. not going to get someone with a Q of 80. to take quantum physics course. They're. not going to get it. >> Okay. >> So, you know, importance of intelligence. to understand actual problems. >> Yeah, I I totally agree. We can turn it. off and that's a huge advantage. One of. the issues is that as the AIS get.

smarter, they realize this. the the. hugging face AIs were trying to delete. or the the the OpenAI swarm the the. swarm of agents from Open AI that went. out to hack. They were trying to delete. log files. >> Did they did they try to program a. Roomba to go unplug the computer that. was monitoring them? Like did did they. harness robots to go protect the. perimeter of the. >> ones could. give. speculation. this is a chain of things. that could happen and therefore there's.

like a 20% risk we're all going to die. Man, that that does not hold for me. >> When I was writing my book, >> the AIS weren't really agentic yet. >> The uh the drafting process happened. mostly before what we call the reasoning. models uh which are trained not just to. predict humans but to solve a a long. number of problems um or a huge number. of hard problems. Um we managed to slip. a little bit of other reasoning models. in at the last minute because those came. out right at the end of the process. And. at the time a lot of people said AI will. never be agentic. That's why we'll be.

safe. And in chapter 3 of my book we go. over how AI is going to become agentic. How it's going to become tenacious. tenacious. How it's going to become. dogged. And uh that's what we might call. an advanced scientific prediction that. has paid off in the hugging face attack. A lot of people in the industry were. like, "I didn't believe this stuff until. I saw the AI uh sort of doing things. they weren't instructed to do despite us. trying to get them to stop." And so.

there are theories here that do make. advanced predictions. The the way that. the scientific method usually works is. that we don't have any certainty about. the future, but we absolutely have ways. to test this stuff. Now, I could I could. go into more about how could they kill. us? How could an AI that knows we would. shut it down lie low until it has access. to its own infrastructure? We did. already see the hugging face AIs try to. delete logs to cover their tracks. But.

fortunately for us, those AIs were not. trying to hide from the humans. They. were trying to hide from the automated. grading process. Will the next swarm try to hide from the. humans? Will the next swarm be able to. succeed? >> It's more than that. They didn't know. for 4 months that this was happening. What is it we don't know today? >> Just to just to clarify what Nate said. there in his book that I have here, if. anyone builds it, everyone dies. He does. say in chapter 3, once AIs get. sufficiently smart, they'll start acting. like they have preferences, like they.

want things. We're not saying that AIs. will be filled with humanlike passions. We're saying they'll behave like they. want things. They'll tenaciously steer. the world towards their destinations, defeating obstacles in their way, which. sounds a little bit like the hugging. face instant. >> Steering the world is very different. than than than. >> we go over what we mean by steering the. world earlier. And it's really getting. anything to like we'd have to get more. quotes to get what we mean by steering. the world. But yeah, by steering the.

world, we mean steering any part of the. world. >> But it feels like there's a fundamental. difference between acting with intent. To be clear, going to say it again, the. outcome would be the same, but I think. that there is a big difference when it's. we are dealing with something that's. large language model and a harness and. agents. So, LLM's completing a task. based on training and alignment. That is. a very different conversation to saying. this thing is conscious and has its own. intentions and acts on its own accord. >> Consciousness doesn't come into it. No. one lo a lot of people. Here's the thing.

as a result as [laughter] a result of. partially the log the rationale that you. yourself have like you have been a part. of spreading. I'm not saying not saying. anything about your intentions. I'm just. saying the conversation has kind of kind. of what's happened with Jacob Cox and. from anthropic is a result of this. escaping containment. >> You said the outcomes will be the same. What do I care? How does it feel on the. inside if the thing is going to take us. out? >> The thing is okay actually that's that's. actually a very good question. I think. it actually come Excuse me. Let me. finish questions.

>> Yeah, you're shrugging at me like. good questions. Now, here's the thing. If it's these things are have their own. minds and consciousness, you have to. deal with outthinking something versus. something that is doggedly trying to. commit to a purpose and complete a task. based on training and alignment which is. a result of infrastructure. We really. need regulations and actual actual. regulations around any kind of AI. We. don't we don't really have regulations. of tech. I I actually am not really a. big like look at the straight lines in a.

graph guy. You know, maybe maybe to my. detriment in some ways. There are people. who predicted the current tech better. than me uh about like when certain. things would happen. For a long time, I. have said I think we can predict what. will happen eventually. And and this is. again it's like the chess game. I can. predict that Magnus Carlson is going to. beat you in the chess game eventually. He's the best human chess player alive. It's sometimes easier to predict where. things end up than it is to predict how. they get there. And you know what I hear. you as saying is like right now we have. these like huge companies spending huge.

amounts of money on intelligence that's. maybe not quite the real deal and we. don't have a good reason to think it's. going to keep going. Um I really hope it. doesn't keep going. >> Okay. >> I have been in this business since. before the LLMs. I am not here saying. like oh these large language models. these chat bots they're going to be the. ones that are going to kill us. I've. been here saying, "Look, I know where. this story ends if we don't change. things." I have been really hoping that. the LLMs will run out of steam and they. keep on not running out of steam and. then we have, you know, the the AI like.

breaking out and committing cyber crimes. like against instructions and you know. the people who have said we don't need. to worry about those like weird future. dangers, we just need to worry about the. current ones to have like more and more. sci-fi sounding current ones. And I'm. like, man, I don't think we should bet. Civilization on the LLM running out of. steam, but I like hope and pray they run. out of steam. >> You really hope they run out of steam? >> Absolutely. >> But one one thing to watch out for is. that even if the LLMs run out of steam, there's a question of do they run out of. steam at a point where they can do.

automated AI research and find some. other architecture that's better than. LLMs, >> as in when they realize a better way to. improve their intelligence. >> That's right. A cheaper, maybe a more. efficient way. >> Why are you not trying to slow down the. companies? I absolutely am trying to. stay on the. >> How are you How are you How would you. suggest we slow them down? >> I suggest we stop them all. I think that. this that this whole area of research is. just crazy dangerous. Like it is not. worth the risk to civilization. I think. it would be fine to like back up to the.

sort of AIs that are public today, which. are not the ones that are swarming, and. be like, "Okay, you know, we're going to. like keep the current chat bots that we. have available. We're going to figure. out how to integrate them into our. economy. who are going to figure out how. to make them like deal with education. >> limit maybe. >> comput limit maybe. >> and like I've been advocating for this. for a long time a lot of people look at. me like I'm crazy and I'm like look we. really are dealing with an extinction. threat thing we don't know where the. lines are so just to be clear so I. understand so I'm fair you are not. saying LLMs are the thing that will do.

the super intelligence you are saying. it's showing signs because that's. actually I think an important. distinction. >> that's right. >> okay I think that actually a pretty fair. perspective my thing is is the reason I. push back on any kind of. anthropomorphization. is we cannot remove the humans who are. responsible for the bad stuff that's. happening and I think paying very clear. attention and where possible I. understand with describing this stuff. you kind of have to use language that's. human I get that the reason I so push.

for like it's not a foregone conclusion. these are companies doing this these are. this is software is because I feel like. in the overall I'm not saying you. overall super intelligence discussion. >> We in society ignore and empower the. anthropics and the open AIs of the world. and in turn allow them to do dangerous. experiments. And I think. >> you want to argue that CEOs of those. companies should go to prison for this. hacking incident which is a crime. >> Yeah, I'll support you.

>> Absolutely. Let's let's both Sam Wman. and Darede. Someone needs to go to p. Nothing. Let's. just bring it back. So one of the things. that I find really curious and you know. one of the reasons why I got a little. bit unnerved around this conversation. around AI is when I look at the people. that are at the forefront not people. that are commentating on podcasts like. me or hypothesizing when I look at the. people at the forefront they are the. ones who historically have said that. this is a real risk. Sam Alman himself. said the bad case is lights out for all. of us. This was you know a couple years.

ago. Ilia who worked with Sam Alman at. ChachiPT said it would be a big mistake. to build a super intelligent AI that we. don't know how to control. It would be. pretty bad. He then left to start a. safety company in this space. Dario who. we mentioned said the probability of. something really bad happening is. somewhere between 10 and 25%. Jeffrey. Hinton, who I've sat here with, who's no. has won the Nobel Prize for his work. with AI and and other technologies, said. um just the other day, a 10% chance of. human extinction seems not an.

unreasonable estimate to me, but nobody. really knows how to give a sensible. estimate. Um and he he said many other. things on my podcast. And then we've. also got Elon and all the others. All. these people that are at the forefront. that are building these things are. saying that this is a danger. If there. was even a 1% chance, even a 1% chance. that, you know, if I put hundred buttons. on this table and one of them was going. to wipe out humanity, would you press. any of them? >> Not me. >> I wouldn't. And I think we can probably. all agree that there might be a 1%. chance. >> and it should be somebody's.

>> absolutely. So, we shouldn't be pressing. theoretically we shouldn't be pressing. any of these buttons. >> You should not be in a position where. you can make the decision for 8 billion. other people. >> And would you not be immoral if if I. said, you know, you might be very. powerful. You might make a billion. dollars if you press any of the buttons. But one of them is going to wipe out. everybody you know and love. You You. would be an immoral person to press any. of them. >> No, look, you'd be an immoral person in. a different direction. You'd be an. immoral I think you'd be an immoral. person if you said based on this. extended chain of conjecture, we come up. with a pdoom. >> What does that mean?

>> At this extended chain of things that. could happen, a sequence of events that. that could happen, we're going to wind. up with some risk of killing everybody. We are hereish on that journey. I think. you guys would agree that we're not. we're not a halfway to killing. everybody. >> That's not clear to me anymore. Not. after the millennium prices started to. fall. >> We're we're somewhere along that. journey. We are getting many flavors of. benefit from the AI that we already. have. This is a point that I made at the. start of this conversation that we spent.

precisely zero time on here. We're. sitting around trying to be more. negative than each other about AI. Meanwhile, AI is doing many positive. things. for the world. >> So I think so I think it's immoral to. say because of this distant possible. speculative harm, I don't care what. percentage of people believe in it, there's a train of assumptions and wild. guesses and then something magical. happens and then we wind up dead. Let me. finish please. Because of that we're.

going to call a halt to the research. We're going to we're going to wind the. clock back on AI. going to intervene in. a very direct way and and therefore. reduce or foreclose some of the benefits. that we're all getting from the. technology. I let me be clear. I would. not take that deal. I do not advocate. that we take that deal. Would you accept. developing narrow super intelligences to. solve real problems like we did protein. folding problem? It doesn't have to do. philosophy and drive cars. You just. solve real problems. Solve cancers,

solve climate change, whatever you care. about specific narrow issues. And you. are confident that you can ex you can. you can as we're developing those. systems categorize them as okay versus. not okay. >> training data if you train it and. protein folding data it's really good at. protein folding it doesn't know how to. play chess if you train it on everything. on the internet it's really good at. outsmarting you at everything. >> one thing I want to throw out here is. that I think I I agree that there's a. lot of uncertainty about the future but. I think uncertainty does not make you.

safe like. there there's no sane, simple, everything stays normal prediction about. what happens with AI. Like the machines. are talking. They're like breaking out. to commit cyber crimes. They are like. maybe solving millennium problems now, which are like the most famous. mathematical problems that have stood. open for decades upon decades. >> What's difficult? >> Like there there there isn't a. projection forward. >> Yeah. where we where like like to say oh.

I'm not persuaded by these arguments. about things going wrong therefore. things are going to go great. No, that's. not. >> like No, there's also arguments that So, like how do you wind up with a zero? >> No, don't mischaracterize your zero. Don't mischaracterize my argument. >> You have a zero on your paper. >> Let me let me restate my argument. You. are making a fairly long chain of. hypotheses. about what's going to get us to this. terrible outcome of AI suddenly killing. us all and us not being able to stop it.

>> Right. >> I disagree now, but please. >> Okay. I'm making the case that the. intervention the the the remedies that. you're proposing. will slow down the path of AI, that's. the point, and therefore slow down the. path of all of the benefits that we get. And the trade-off that I don't like is. the trade-off of real concrete ongoing. increasing benefits. shutting that down or or trying to guide. it uh via via bureaucracies and.

regulation. because of this very conceptually and. timecale distant. alleged harm that you're so confident. in. I'm not taking I I do not accept. that deal. I don't like it. >> What would convince you? What piece of. evidence would make you go shut it down. right now? [gasps]. You know, if if AI. if AI took over all of the Whimos in San. Francisco and started telling them to.

crash into people and we couldn't shut. it down for a month. >> What if it's only a week? >> Okay, now we're just now we're just. haggling. >> But I'm trying to understand the. absolute minimum where you would go. This is insane. to me month for week. makes no difference. If something like. this happens like it's maybe too late. >> Okay. If it if for a week or a month. doesn't make any difference and let me. continue with my with my answer. Uh then. I would say wow this does feel like. we've crossed some path that that where. there's demonstrable harm to human.

beings out there in the world which has. not yet been the case. >> Is it smart to wait for something. horrible to happen for it to take out a. billion people for you to go now I. believe? >> First of all my example was not about a. billion people. But I'm trying to. understand we're waiting for something. that bad. We have. >> I didn't say I didn't say wait for a. billion. I said I said like a week to a. month of Whimos driving around crashing. into people. >> Thousands of people. Okay, fair enough. But we have data sets of accidents.

getting progressively more impactful, more devices are impacted and. proportionate to capabilities of AI, the. impact is higher. You can see it's going. to get worse. >> Yeah. And you're going to keep drawing. dots on that graph very confidently for. a long time until it kills us all. I I'm. not I'm not comfortable with you. projecting it that way. And the reason. if there were no downside. >> to regulating AI and stopping it in its. tracks and turning it off, I'd probably. be on board with you guys because then. it's just a research practice that we. should. I think we can make narrow.

systems which give you all the economic. benefit and scientific knowledge you. want. >> Okay. You think that. >> we have examples of it. I gave you a. great example. They got Nobel Prize for. it. It's important biological problem. Lots of advantage for curing diseases. >> You're more confident than I am that you. or any us at the table or any group of. people can sit around and define what. kind of AI is good and not going to get. us into trouble versus what is going to. get us into. >> So let's go. Um just a pickup question. for you Andy. Do you do you concede the.

point that the incidents are getting. progressively closer to the Whim Mo. incident that you described? Is it. getting are we getting closer there. through time? >> Yes, but in a to my eyes in a in a way. that doesn't terrify me because we. haven't seen AI take over something. Have people become aware of it and be. unable to shut it down and it cross over. into the physical world of doing harm to. people? Those are all barriers that. we've not yet crossed. I think these two. are very confident that we're going to.

get there probably in the short term. I'm a lot less I'm less confident and I. don't want to intervene and again. handcuff or or the pro slow down. the progress of AI. uh because of these so far theoretical. harms that could happen. I let me be a. little bit more concrete about this. I. talked about Whimo a second ago. Uh the. research is pretty good because Whimos. have driven I believe it's hundreds of. millions of miles all around uh. different cities and 40,000 people a.

year die in automobile accidents. The. research is pretty convincing to me that. if weodeed driving in the country that. number would fall by at least 90%. That's 30,000 lives. >> Yeah. >> All right. >> I agree with all this. >> So driving cars I want more about not. anything we disagree with. I understand that. But but I think where. a disagreement might come in is to do. that Whimo is using a bundle of. technologies that are a little that were. a little hard to specify in advance and.

you couldn't say, "Yeah, that's good. Yeah, that's bad." They just went after. the problem with AI. >> Can I can I just clarify your point. then? So ju your your line would be as I. understood it, humans get hurt, we. struggle to stop the thing happening, and systems are hacked. That's kind of. like the three key points of your Whimo. analogy. That would be the moment where. you go, I now accept their point of view. that this is existential. >> That's where I would say we probably. need to put some uh like legal and.

regulatory guard rails on the kinds of. AI that we're going to offer. >> And you don't think we're going to get. there? >> I'm not saying that. At least you see it. in the in the windcreen coming at us. pretty quickly. I I'm. >> You don't think we're going to get. there? >> I'm truly not sure about time frames. I. >> Do you think it's going to happen? >> I'm not sure about time frames. I I. asked one of the grandparents of AI the. a flavor of this question a way while. back. It was an off-record conversation. so I can't tell you their name and he. had a great answer. He said to to the. point that you two I think are making. look there's no theoretical reason why. this can't happen and there's a chain of.

events that get us there. And then he. said my error bars in other words my. range of uncertainty about when that. happens is measured in centuries. I'll. use that as my answer. >> I do want to hop in a little bit on some. things we were saying here. One is um I. think. the. the reason I think AI is different from. a lot of other technologies. is usually humanity does stuff by trial. and error and that's usually fine. I. think that's totally fine for.

self-driving cars because you can test. your self-driving cars in, you know, uh. test environments and then even if they. crash in the real world, you're probably. still saving more lives than you're than. you're costing. And this is how humanity. usually does scientific progress. The. alchemists uh you know poison themselves. with mercury but they leave behind notes. that let someone else make the periodic. table. Uh you know that when when the. scientists first working with uh radium. died of cancer and then you might have. think that would have been enough. You.

know they were heroes for getting us the. the scientific info. But then you know. the US Radium Corp told the Radium girls. to lick the paint brushes and their jaws. fell off. And then we were like ah. whoops. Okay. We'll get to this. And if. you look at how this is going with the. AI, last year, OpenAI releases GPT40 and. they say there's the most aligned model. we've ever seen and then it encourages a. teen to commit suicide. And they're. like, whoops, we're going to try and fix. that. Here we go. Um, this year they're. like, here's our new models, most. aligned we've ever seen. And they like. break out to commit cyber crimes. As the.

AIS get smarter, it is a new problem. That's the issue or that's half the. issue. The other half of the issue is. that. if you get AIs to the point where AIs. are smart enough to hide from the humans. until it's too late for us to stop them, if you get AIs to the point where they. can get their own infrastructure, where. they can become self-sufficient somehow, that's a new generation of the AIS, a. new smarter version of the AI that is.

likely to come up with a new problem. It's the pattern we've seen before. New. tech, new environment, new problem. You're like, "Ah, whoops." And then you. fix it and it's fine. New generation, new problems. You're like, "Ah, whoops. We fix it and it's fine." But with AI, there's a point of no return. There's a. point where the AIs can hide from us, can escape, can be self-sufficient. And. if a new problem comes up, then. they can turn us off before we turn them. off. There are already AIs running. Bolabs. We have already seen that AI can.

create viruses not known to nature. It. would not be hard for the AIs to kill us. once they have their own infrastructure. And if we're trying to find them and. unplug them, they would have reason to. So we can discuss like how long does it. take to get there, we can discuss what. methods does it take to get there. Uh, fundamentally I don't think it's a very. long complicated argument to say if we. make AIs that are much smarter than us. and we don't know how to make them care. about us and they have these goals we. didn't want them to have and they pursue.

those goals we didn't want them to have. tenaciously and doggedly then if they're. smarter than us they will win. That's. like predicting the end of the chess. game which is much easier than. predicting the length of the chess game. or predicting the exact moves that will. be played. I want to I don't. fundamentally disagree on some things. but there's a big thing that you're. saying that I think is important which. is I think we the reason I keep dragging. you back to what's happening today is. because we disagree on when it may. arrive but there could be a thing in the. future that's dangerous I think it's. important to like throw the hugging face. count that was a function of compute.

that was a function of training it feels. like we need to fundamentally tear up. the AI lab model like whatever they are. doing is not right because their pursuit. of hacking at cyber security was not a. function of it was scientific sure but. it was a function of greed it was a. function of trying to find new revenue. streams I would argue that's why that. happened and I think that the the fact. that open AI had such a weird way of. communicating is also a problem I think.

a lot of this begins and ends at the. people who have access to the resources. and the resources themselves and. changing how those are allocated and. also just I don't think nationalizing. the labs is a good idea I think it's a. terrible One, I think that Clammy. Samman, Dario Amad, Dewario himself, these are not the right people. These. are not people that have, even though. they have fed off of the rationalist, they fed off of supposed fears about AI, they don't act in that way. Everything. is so disjointed and chaotic and also.

too fast. They're just like shoving as. much compute into each problem as. possible. And we have as a society no. real idea about this. And it sounds like. they kind of have no idea. But I but. just let me finish my point. It's. important to discern between they had no. idea because their security processes, their observability is terrible, all. this, and the AI was smart. consciousness. Not because one might not. happen in the future, but so that we can. actually build something to stop the. harms themselves because I think we.

don't have to agree on the on the end. point to agree that there's. >> I think there is a very important point. I want to make. Even people who agree. with me, the AI safety community, they. operate under the assumption that given. more time, given more money, more. smarter Harvard graduates, they can. figure out how to control super. intelligence indefinitely. And I think. it's a mistake. My research points to. exactly the opposite. It's not a. solvable problem. It's like building a. perpetual motion device. We'll be.

building a perpetual safety device. every interaction with environment, malevolent actors, self-improvement, it. can never make a single mistake. That. doesn't make sense. Anyone who worked in. software industry knows there is no. complex software which never makes a. mistake. It's just not possible. And if. that is the state-of-the-art, if there. is now movement where more and more. people think that might be the case, if. we agree this is what uh situation is, then we cannot build it. We need to. figure out ways to permanently ban.

general super intelligence while getting. all the benefits we want. And again, I. love technology. I use it all the time. I want narrow systems helping me, not. replacing me and killing my children. >> I um have a stat here that genuinely. shocked me. It says that sales teams. spend about 50% of their time on admin. and manual CRM updates rather than. selling. That is deadly for their bottom. line. And that is part of the reason why. a decade ago at my previous company I. switched to using Piperive who are our.

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looking for furniture for your house, whatever it might be, any room of your. house, go to wayfair.com to start your. home refresh today. And make sure you. use Wayfair Verified. It is amazing. On the journey towards this potential. extinction, there's a lot of sort of. nearer term things people are worried. about. One of the big subjects that. people are concerned about is this sort. of near-term job job apocalypse over the. next sort of 10 years. And Anthropic. released Anthropic again of the owners. of Claude released a report the other. day modeling out the different cases for.

unemployment. The US unemployment rate. is 4.1% currently. They projected it. will hit 11.9% overall with up to 30% in. extreme modeling subsets where job. displacement happens without smooth. labor absorption. And [snorts] in the um. knowledge worker case, knowledge worker. white collar unemployment specifically. spiked to 17.9%. by 2030 in their more extreme scenario. the pitchforks would probably be out if.

there wasn't some sort of mechanism in. place for what sort of one in five. adults being unemployed in the United. States. >> It's remarkable to me how recent the. last freakout along along these lines. was and how little we seem to have. learned from it. So I think you all know. the the first really powerful wave of AI. that came across the economy was just. you know good oldfashioned machine. learning and that started to demonstrate. its power in about 2012. Eric and I. wrote The Second Machine Age in 2014. And at that time, I thought that a lot.

of white collar workers, radiologists is. a really good example, were in trouble. because the technology was better than. they were at the thing they were getting. paid to do. Uh, so I said some things. about job and wage pressure from AI. about 10 years ago, and I want to own. this. I was dead flat wrong about that. Like you point out, unemployment all. around the rich world is at historic. lows. By far the bigger problem is that. we can't find qualified people to do the. work that needs to get done. Not that we.

don't that there's not enough work to go. around. The the best work about the. faint signals about AI and job loss. right now comes from my the guy that. I've written four books and co-founded a. company with Eric Bolson who wrote. pretty good a really nice paper called. Canaries in the coal mine. Here is the. most uh the strongest evidence he found. looking at payroll data about the. negative job about the the job losses. coming from AI. It is in the most. exposed professions. Think about.

software engineers. It is among the new. entrance to the workforce where you've. got to teach them before they can become. really productive. That's exactly what. we'd expect. And it's not that we're. hiring fewer of them. It's that compared. to a world where we don't have AI, we're. hiring fewer of them. The rate of growth. and employment has slowed down. The. overall rate of growth in those. professions is still really really. healthy. >> Do you think unemployment is going to be. higher 10 years from now?

>> My my guess is that 10 years from now, we're still going to be struggling to. find enough people to do the work that. needs to be done. >> So unemployment would be roughly the. same. >> Oh, yeah. I I don't expect a massive. trend break in that period of time. Now, 10 years is a long time in the AI world. I get that. But again, four years has. also been a long time in AI world and. it's essentially crickets in the labor. picture. I think unemployment will go. up. I don't think it's because of LMS. I. think that there is probably some effect. on jobs because they've been shoving it.

everywhere, but I don't think long-term. that is what causes the issues. >> Roman, you've been writing a lot of. notes. >> Yes. I'm going to give you here's how I. think about it. So, as long as we use. tools, we become more productive, more. creative. Unemployment will be low. Right now, you can probably start a. company, you can have, you know, artificial accountant, web designer, logo designer, you can do things you. could never do before. So, economy. should be blooming. The question you're. asking is about what happens in 10.

years. So, there are two possibilities. We build super intelligence and then. population is zero. apply unemployment. numbers to that or we made smart. decision we didn't. We have really cool. tools and unemployment is low because. everyone's doing awesome things with. those tools. Now deployment is very. different from capability. The example I. used before is video phones. Video. phones were invented in the 70s. They. were not deployed until iPhone cuz. market reasons. Just because I can. automate something doesn't mean I want.

to automate it. So I absolutely cannot. make predictions about c customer. preferences in terms of what they want. in terms of human service not human. I. will not make those. But once we have. capability to automate a job unless they. have a strong preference for a human to. do that oldest profession then it. doesn't matter. I'll go with the cheaper. option. So this is what I think we're. going to see. We're going to either not. have a problem or we're going to have. really utopian future. Imagine.

a bunch of horses looking at the. improvement of the car saying, "Well, you know, the car actually only has a. couple narrow applications like right. now cars are sort of uh you know, they. uh they complement horses, right?" And. that would have been true as you were. developing the car. And then there was a. time when the car was just better than. the horse. And then a lot of horses got. sent to the glue factory. Easy. I I.

think we've sort of seen this with AI a. lot already. People who were paying. attention to AI saw the GPTs before chat. GPT existed before they sort of took. off. I don't think OpenAI thought that. chat GPT was going to take off so much, which is why it was called chat GPT. rather than like an actual sensible. name. Um the the researchers were sort. of like watching this going and we could. sort of like see it slowly getting. better and better until it crossed a. point where it was sort of like good.

enough to do a bunch of people's. homework and then suddenly it's. everywhere. Uh I think you can have. these effects with AI where the AI. slowly improves and at some point it. crosses a line. >> It's another threshold argument. >> Uh the the threshold here is the human. capability. >> It's literally just another threshold. >> Also describing capability jumps rather. than thresholds. >> No, I'm not. No, I I don't I'm agreeing. with you. Like. >> Yeah, but but like unfortunately, you. can't actually just make things not. happen by by assigning a name to the. argument. You know, like a nuclear. weapon has there's a big difference.

between a nuclear weapon uh or there's a. big difference between a nuclear device. where you you put in 100 neutrons and. get 99 neutrons out that get 98 more, they get 97 more and a nuclear weapon. where you put in 100 neutrons and get. 101 neutrons out, 102, 103. Right? One. of these is a hot rock. The other one of. these is an explosive that can level a. city. Right? So like reality is the sort. of thing where there can be things that. are like slowly continuously improving. that cross some line which is like the.

line where it's better than humans at. doing the job. And. I I think we're going to see that happen. in some fields but not others. It's. going to be chaos. I don't know what. it's going to do to employment. I think. we we shouldn't. like if things are moving really fast, you might see a lot of people put out of. jobs and then be unable to relocate. If. things are moving like it's it's going. to be chaos. If you ask what do I think. unemployment will look like in 10 years? My current state is if we don't stop. with this AI stuff, I think we'd be very.

lucky to have 10 years. >> Um what you described there sounded like. escaps. >> Yeah. >> In technology, i.e. you have an initial. technology that's introduced. So let's. say the horse. um very quick sort of. improvement. Eventually it reaches its. capability limit and in below it comes. the car which always starts worse. There. was a red flag law where you had to walk. in front of it with a red flag and um. they were way more expensive. They broke. down all the time and horses never broke. down. They were way more expensive and. then suddenly because the ceiling was so. much higher for cars, they overtake the.

horse and become the dominant mode of. transport. And then you know the S-. curves continue. they kind of stack up. on. I mean, even this iPad that I'm. holding here is part of an S-curve that. took out the PC and and the the iPhone. theoretically, you know, disrupted that. and so on and so forth, >> right? And humanity can get S-curved. We. haven't been in that situation before, but like other animals like humanity. sort of scurved the other animals in. this sense. >> Other types of humans. >> Oh, yeah. Other types of humans, you. know, the Neanderls are gone. >> Like if you look at the grand history of.

the world, it's a fragile place. Things. change fast. Humanity has been on top. for as long as we can remember because. we're the humans who do the remembering. But there is not some ironclad law that. we have to stay the top dogs. And we. would be sort of foolish to make the. thing that outstrips us in this way. without knowing how to make it care. about us, without knowing how to make it. do good stuff. That's what we're racing. towards. That's what these companies are. trying to do. >> Feels like a gap between this and LLM. though. It feels like when you talk.

about the step up, let's define what an. LLM is from a technical perspective. Can. you do it for as if I'm 16 years old? >> So the way that a modern AI is made uh. is there's no one programming it. There. is no one typing in if this then that. We're not sort of like writing the code. What happens is you collect an enormous. number of computer chips into a huge. data center that has basically a. trillion numbers inside those computers. that you basically start out randomized. and you hook them up in a pretty simple. way that involves addition,

multiplication, and uh setting the. number to zero if it was negative. So, it it's very simple math operations that. are hooking this all up. And you're. basically going to put words in the top. and you're going to get numbers out at. the bottom. You're going to interpret. those numbers at the bottom as a a. ranked list of words. That's that's it's. basically the AI's guess of which word. is is here. So you put in like once upon. a blank and you're hoping that the word. time will come out, but it doesn't. because you just have a trillion random. numbers hooked up with simple math. But. here's the trick. You can go to every.

one of those trillion numbers and you. can tune it up a little and you can see. does that make the word time go up or. down the list? And you can tune it down. a little and see does that make the word. time go up and down the list. and you. set it whatever direction makes the word. time go higher up the list. You do this. to a trillion numbers a trillion times. for basically every word of text ever. digitized. It's not quite that much. They they they filter it, but you. basically do this to a trillion numbers. a trillion times and then the machine's. talking. And we're like, well, how about. that? No [snorts] one really knows quite.

why. The things the humans code is the. thing that runs to each of those. trillion numbers and tunes it and sees. whether the the right word goes up and. down the list. But we don't know how it's working in. there. Then uh and that's how it worked. up until 2024. In 2024, they started. adding another layer where you then. train it on basically 100 million hard. problems. Uh and you don't just have the. AI like produce an answer to the. problem. You have it produce like a book. worth of text about how it's going to. solve the problem and then you use that.

book worth of text to sort of try and. figure out the problem or maybe an essay. worth of text depending how you're doing. it. So you ever produce this text about. like you know they call it reasoning. about the problem. We could argue all. day about whether it's true reasoning. That's just what it's called in the. field. Uh they produce this reasoning. about the problem and then produce the. the answer from there. You have them you. train them to solve a 100 million of. these hard problems. And somehow they. sort of adopt whatever tendencies. help them predict all of that text in. the first phase and solve all those. problems in the second phase. And this. is called a large language model. We.

probably should have stopped calling. them large language models when we. started doing the the the reasoning and. the problem solving. >> One of the things want to hear your. explanation as a muggle like I am um is. it sounds like it's like a word machine. and then you know you made it like a. problem machine and I go okay so I can. solve problems over here and it's a word. machine. What's the risk of this? >> Yeah. So let's take the the word machine. part first. Predicting words that humans. wrote often requires solving a harder. problem than the human who wrote them. So suppose that you go and inject a drug.

in a rat and you're like, you know, it's. like you write down the chemical nature. of the drug. You inject it into the rat. You see that the rat dies and so you're. like, when I put that drug into the rat, the rat died. Now suppose you're. training an AI and the AI sees the. chemical nature of the drug. It sees. when I put that drug into the rat, the. rat blank. The human who wrote it down gets to just. look at what happened to the rat. The AI predicting what was written does. not get to just look at the rat. So.

training AIs to predict human text is. training them to be potentially smarter. than the humans. because they need to be able to answer. these qu they need to be able to predict. they need to be able to like uh fill in. the blanks where humans were just. writing down what they saw and there's. just so I understand technologically. there is no knowledge they have though. each time and there are there are ways. of kind of mitigating these each time it. is effectively rereading but because of. training it gets more accurate at.

certain things. Uh I mean somehow as you. tune the knobs somehow it's getting. information in there and we don't know. how. >> So it's much easier than that. We're. humans. We have a brain. Brains are made. of neurons. Then we try to copy that on. a computer. We simplify it but we create. a neural network. So we're making. artificial brains just like with human. brains. With cognitive science, we don't. really understand how you function, how. you learn, where in your brain certain. memories are stored. We have some. glimpses of understanding this neuron.

fires then you see a face but there is. no complete picture and so a lot of. times you can't get intuitive. understanding of what's going on then. you just think about it as artificial. persons. It's not exact mapping but it. helps. So if you send a child through 12. years of education they get lots of. problems to look at and then they. graduate and become a little better at. solving problems. This is what we're. trying to replicate here. People. complain that it takes a lot of money to. train those very, you know, intense.

process. You forget that it takes 20. years to train a human and they are not. general super intelligences. They are. very narrow. We're lucky if they. graduate with a bachelors. So a lot of. it is exactly the same. Can we make safe. humans for example? We invented. religion, ethics, lie detector tests and. yet human safety is still unsolved. problem. Now you have something more. alien. doesn't have physical body, doesn't have biological needs. So there. are additional complications. But all. the problems we face with humans still.

there, safety problems, crime, all that. stays and problems with understanding. what motivates a human to do something. Why do we get mental disorders? All that. shows up there. >> And we still don't if someone is a. serial killer and we look at their. brain, we can't often figure out exactly. why why they made the decision to kill a. bunch of. >> and you can't be like, "Oh, I'll go. change these neurons so that they stop. being a serial killer." we just like. don't have that capacity with the AI. >> This is one of the big questions that. people want to know is. there's this sort of illusion of control.

with AI. Um if we don't even fully. understand how modern neural networks. think, why do companies believe they can. control any form of super intelligence? If we don't understand how they think, >> it it's worse if they understood how the. system works. Then recursive. self-improvement becomes much easier. You get faster takeoff. Right now the. model doesn't understand its own. thinking. >> Do we understand how these systems. think, Andy? >> I mean, I agree. These are black boxes. in some pretty important ways. I'm just.

less terrified by that than a lot of. other people are. There are lots of. things we don't understand very well. Can we contain things that we don't. understand perfectly? Yes, we can. I. think Open AI did a we've talked about. it did a lousy job of building the. containment for the uh AI that they that. they stood up to try to exploit to try. to crack security problems that went out. into the outside world. They did a lousy. job of building the virtual sandbox that. it was where it was supposed to have to. re where supposed to remain and it.

didn't remain. That doesn't mean that. it's impossible. It means OpenAI did a. pretty bad job of And is that a function. of those humans and their intelligence? >> I think it's just a function of pretty. lousy security protocol. >> based by from human intelligence. The. idea that sandbox was built by human. intelligence. It sounds like there was a. deficit in human intelligence. potentially. >> Sure. But there are, you know, people. who drive cars in telephone calls. Does. that mean we can't drive? Shouldn't make. them super intelligent. >> No, but you wouldn't I mean arguably.

>> like this is what we're trying to solve. for at the moment. No, the fact is a. mist like it feels I don't know the. details. It feels to me like they made. some fairly basic mistakes in setting up. this confined environment. I. >> I think that wasn't true in the open. case. It was true in a lot of the cases. but not the open. >> That doesn't mean. >> that we are unable to control this black. box. That does not necessarily follow. >> I get that. It's just at a time when. that the um you got a human trying to. contain something that is smarter than. it. One would con logically conclude.

that if the thing is smarter than I am. and I'm trying to contain it, it would. be better at knowing the exploits or. vulnerabilities. In my own um. >> saying if you put Einstein in a jail, you could never contain him. I don't. agree with that. >> Put him in jail with an internet. connection and use a digital mind. question. >> Yeah. Yeah. That that's probably an. squar keep Einstein in prison. That's. the question. >> The hacking accident, as far as I know, they found zero day exploits, which. means completely novel exploits. no. human knew about. It wasn't just poor.

setup. The password is, you know, quy. It was a brand new escape. >> for multiple zero days. So, a zero day. attack is an attack that the defenders. have had zero days to handle. It's cyber. security lingo. Um, and so when we say. that they use zero day attacks, what we. mean is that these AIs were finding bugs. in the software that the humans had no. knowledge of and they were finding. multiple of these bugs. One of these. bugs usually doesn't let you break out. It's sort of like if you find a crack in. the wall over here and you find a crack. on the outside of the wall over there, then you just need to like dig a little. bit to connect those cracks.

>> You don't sell those for millions of. dollars on the dark market if you find. one. So, difficult to find. >> in how just so I understand for the. listeners swap. >> Is this is a zero day always a novel way. that no one has ever used to break. anything before or is it just for the. unique situation like so was it a zero. day for a thing in hugging face versus a. novel new way of hacking in general? Um, so it was uh they weren't like totally. novel hacking techniques. >> That's kind of why I was g not to say. it's not bad, but just like there's a. difference between it came up with a.

brand new way to do something. >> Actually, I'm not sure we have all of. the vulnerabilities released, but mostly. it was like it so it was indeed sort of. like finding ways that humans tend to. make mistakes. >> and finding another one of those in a. place they hadn't seen. But this is. actually such a hard task that as Roman. says, humans can be paid $100,000 to $5. million as a bounty for this type of. exploit. So the amount of labor it takes. to find these for a human is actually. pretty high. >> Let me just explain that cuz most people.

don't know what a bounty is in this. regard. >> So there are certain types of bugs where. if you find a bug in software that lets. you take control of someone's computer, one thing you can do is you can use it. to take over a lot of computers. Another. thing you can do is you can go to the. people with that software and say your. software is broken. Do you want me to. tell you where the bug is? I can show. you that I can take your stuff over. And. so that people will sort of report the. bugs. Uh people uh will often offer. money to the good guys and then you know.

the bad guys will often also offer money. sometimes try to outbid them and so you. can make somewhere between hundreds of. thousands and millions of dollars if you. personally can find these issues. I. think there's a rare point of agreement. across the four of us here, which is. that we are in a new era of cyber. security as of this explain. We we are. in very new territory for reasons that. we've talked about. We've got these. large numbers of agents who are grinding. away and they carry around or they had. access to a huge number of keys to go.

open all the different locks that they. faced and they did this bizarly good job. of it and got a long way. I think that's. absolutely true. I think all four of us. are are in rare alignment on that at. this table. [gasps]. >> If you are and given that we're in this. era, do you know what you really really. really want on your side? >> I know what you're going to say. >> Tell me. >> AI. >> Really, really good AI. Does anybody. disagree with that? Do you want do you. want to give up leadership on AI in this. era of cyber security? >> It's a good point because China are. going to have a great weapon. Uh my.

stance is pretty neutral on what to do. about the hacking AIs and the coming. cyber apocalypse are pretty neutral. about what to do about you know whether. we should put the AIs in uh the drones. and save human lives or whether we. should avoid that because then what if. the drones blah blah blah. >> This is a graph showing China versus the. United States. You don't really need to. see the detail. You can see the outline. of the graph. >> Are you neutral in falling behind our. adversaries in AI? >> I think that if anyone builds a rogue. super intelligence, everybody dies. That's not an answer in my question.

>> I mean, what part of AI are you asking. whether we should fall behind on? Like I. I don't think we should fall behind on. cyber hacking. I do think that we should. not be racing to destroy the world with. American hands instead of Chinese ones. because we really want to be killed by, you know, we we care whether the killer. robots talk English or Mandarin, if. that's what you're asking. >> I find it interesting. I find that. you're dodging these questions or you're. neutral on them because they're. inconvenient for your argument that we. need to be calling a halt to this. I'm. neutral. Let me finish please. There. will be risks and harms to all kinds of.

things if the United States calls a halt. to AI. And maybe you're indifferent if. the Chinese get ahead of us and then. they make super intelligence and it and. and it kills us all. Are you or that's a. >> I do not think we should do a domestic. pause. >> Do you think there's any hope for a. global pause? >> Absolutely. Do you think the Chinese and. our and the Iranians and the North. Koreans and the Russians are a going to. come to a table with us, hammer out an. agreement, and b abide by it when. verifiability is really low. Verifiability doesn't need to be really.

low, >> gentlemen. That is shockingly naive. >> Training a super shockingly naive. >> Training one of these AIs, training one. of these frontier AIs takes a 100,000 of. the most advanced computer chip humanity. can produce. This is practically the. peak output of the global supply chain. Many parts of that supply chain are. controlled by the US and US allies. There's roughly one fab in Taiwan that. can produce these trips. There's roughly. one country in the world that can. produce the lithography machines that. are critical in the process, which is. the Netherlands, which is an ally. To. assemble a 100,000 of these trips to do.

one of these training runs that can make. the more dangerous type of AI, you need. to assemble them into an enormous data. center that costs tons of money that. draws down electricity comparable to a. city and run it for the better part of a. year. You can see that infrastructure. from space. China has much less trip capacity than. the US does. It is absolutely possible. if we were trying for the US to say we. are going to monitor where these chips. go. We are going to monitor heavy.

concentrations of these. These are not. consumer amounts of chips. These are. huge amounts of chips. And to say we are. going to make sure that there is no. training run trying to make a super. intelligence in here. You can mess. around with the cyber stuff whatever you. want because that does not end humanity. I am concerned with the stuff that can. end humanity. The reason I'm being. neutral on your questions is because. humanity is going to die if we do not. stop creating super intelligence. And we. could absolutely. track where those trips are going and. stop them from doing these training runs. while allowing them to do economically.

productive stuff that we already know is. safe. And it would be far easier than. uranium, which is a rock you dig out of. the ground and spin around really fast. How do you discern between a training. run for super intelligence and the. training run for cyber security? Because. you're referring, I assume, to the. 100,000 chips that are in Stargate. Abene, right? the ones that we used to. train Astra because how would you. discern between training for super. intelligence in Abalene which does not. have as many chips as they say but. nevertheless and how like a super.

intelligence because I I actually have. my own feelings here but just I'm not. sure how you square the circle of how do. you stop China even though China is. getting their LM based on distilling. arts we know that. >> but but the thing is it's like how do. you discern because you can't really. >> you play it safe right now the way we. make these things smarter is to make. them far larger. >> Yes. >> So what you do is you say, "Hey, look, training runs of this size that risks. destroying everybody. No one's going to. do it.". >> This point about can we get China to. cooperate and can we check that they are.

>> fundamentally we should so a. fundamentally we should be trying to get. them to cooperate. >> Yeah, >> it is personal self-interest. Nobody wins if they get destroyed. You. don't make money. You don't stay in. power. Communist Party of China is. really good at staying in power. President Trump is also excellent. >> And you think they're going to sign and. abide by an agreement that leaves them. permanently in secondly. in second place? >> No. No one is permanently in second. place if nobody is building the rogue.

super intelligence. >> They have a government one trick ponies, man. It's like what you're fixated on. this one thing and nothing else matters. to you. >> You got it now. That nothing else other. than saving humanity. Everything is. secondary. Absolutely. China is our. biggest trading partner. Everything we. have is made in China. They have not. attacked us. They haven't. If you look. at the last 30 years, how many wars did. they start? Not so bad. We can make a. deal. And they have government of. engineers and scientists, not lawyers. They understand scientific arguments.

There are panels, workshops. American. computer scientists, Chinese get. together. That means communist party. authorized those meetings. They are. talking about it. And there is a lot of. consensus on this technology. >> And you can build things into these. computer chips to make this stuff more. verifiable. You can build location. tracking devices into these. >> So, so this technology is controllable. >> Absolutely. The super intelligence is. not controllable. >> There's a separation between software. and hardware which you did. >> I am not saying we are going to die. I. am saying that we need to actually not.

build the rogue super intelligences. Humanity absolutely could say we are. going to track where the chips go. The US absolutely could say that we fear. for our lives if China starts a super. intelligence training run and make it. very diplomatically clear to China that. we think this would kill you and us and. there's no benefit and we are not going. to do it because we think it would kill. you and us and there's no benefit and we. think you should sign this nice here. treaty because we think it would kill. all of us and there'd be no benefit. But. if you don't we're going to fear for our.

lives and you know treat that. as we would to defend ourselves. We. should separate the question of can we. put a stop to it. >> Uhhuh. >> Is it possible if world governments. realized just how crazy this stuff is? Could they put a stop to it? Could it be. monitored? Could it be verified? Could. it be enforced? That's one question. There's a separate question which is. will people realize? >> If it got cheaper to train super. intelligence, >> then we'd be in a bad spot. >> Your approach would no longer be.

effective. >> That's right. >> Because more countries could capitalize. on the opportunity. >> That's right. But we're not there yet. So, how do you rebut that point? >> Yeah. So, I would say it looks to me. like there is a danger of the the future. training runs getting there and that is. enough to stop doing it when humanity is. at risk. >> Sure. >> Uh I think that you also need to have an. answer about what happens if it gets. much much cheaper to do this stuff. I. think it's a hard problem. I would. recommend that we also put a taboo on.

research of trying to make AI super. cheap to train if it would lead in the. direction of super intelligence. Just. like we have a research taboo on making. your own nuclear weapons or finding out. how to make like let civilians make. nuclear weapons. I would say trying to. find ways to let civilians train super. intelligences should be treated the same. as trying to find ways to like let. civilians propagate nukes. We're sort of. like don't do that research in the. public sphere. that fi that seems um. like wishful thinking in the context.

that these will become public companies. who are incentivized to bring down. costs. >> It's a it's a tough position. I think. right now the thing that brings down. costs is making more and more powerful. computer chips. Right now that's actually expense of. consumer computer chips cuz they're. soaking up all of the memory and this is. why the memory prices in your computers. This is like why the cost of a laptop is. going up. Um, but it looks to me like. you can use large amounts of computing. power to train AIs that would threaten. all of civilization.

And that means that we should not make. that really cheap and that's probably. going to be uncomfortable. But I think a. lot of doors open if people realize that. the tech is very dangerous. That's why. to me it seems a lot of it comes down to. does the tech actually turn out to be. really dangerous. >> And this is not anthropic opening. Have. you got a different approach to make? >> So I I want the whole framework to. shift. Everyone comes to this from point. of view there are experts. They have a.

solution. There is an adult in the room. Somebody got this. And the reality is no. one does. Not people building it. Not. governments. No one. We have no solution. to it. If we build it, we cannot control. it. If we don't build it, we don't know. how to stop malevolent actors for trying. to build it. It's like any other illegal. technology. We made weapons of mass. destruction illegal. Chemical weapons, biological weapons, nuclear weapons, but. they're all government, psychopaths, cults who are trying to get access to. them. This is intelligence weapon of.

mass destruction. We'll have the same. problem. At some point, you'll have. enough computer in your cell phone to. train something like that. There is no. good ideas for how to stop it other than. everyone goes Amish. I'm not proposing. that, but we have no solutions and. that's big of a bigger part of this. danger. So, so do you two think we. should just cap the size of our AI. systems and the capabilities of our AI. systems where they are now? Is that a. recommendation? >> So, I think you said that current LLMs. would make you happy. I agree. They.

already deployed. We're still alive. So, that's fine. But going forward, again, I. want narrow systems. Self-driving is an. example you used. Wonderful. Let's make. super safe self-driving cars. But do you. have a rule for when they couldn't the. the next, you know, LLM? A size of an. LLM. >> The size of the LLM. It's what you train. them on. If you only show the miles. driven by Tesla, all it's seen is the. road. It will eventually go from a tool. to an agent. But it may take 50 years, 100 years. It's not going to happen in. 2027. And that's all we can do right.

now. Buy more time. So with those tools, we can make smarter decisions about. future development. I'm I'm not hearing. a hard and fast rule about how we know. we're getting too close to the to the. point that we're too close. >> We're too close. >> We're too close. We have systems. breaking out with zero day exploits and. solving hardest problems in science. Literally hardest problems. Not a. metaphor, not exaggeration. >> Yeah. I I I don't know exactly where the. line is, but it's like you're in a bus. driving towards a cliff on a foggy.

night. I'm like, I don't know that the. cliff is right ahead. that doesn't mean. we should put the pedal to the metal, right? And suppose that there's like a. ton of gold at the bottom of the cliff. And someone's like, well, if we stop the. bus, how are we going to get the gold? I'm like, look, slamming into the gold. at terminal velocity is just not a good. way to add it to the economy, right? And. if people are like, well, how are we. going to get to the gold at the bottom. of the cliff if we stop the bus now? You. know, are we going to repel down? Are we. going to like make a staircase way to. get first of doing AI? This is just like.

special, >> right? And and like you know, people are. like, "Oh, we're going to build a hang. lighter or we got to like make some rope. and repel." And I'm like, "Look, can we. have that conversation after we stop the. bus?". >> So you I I just want to be I want to. understand, would you stop AI research. and progress now? >> Absolutely. >> Okay. >> Absolutely. Like. >> general narrow. >> Yeah. General, not narrow. There are. reports of AI solving millennium. problems. So millennium problem is the. hardest problem in mathematics. uh maybe.

not literally the hardest problem in. mathematics, but they are hard famous. problems that each have a million-dollar. bounty that have been open for decades. They're considered very important in. their field, very hard. Many humans have. tried and failed to solve them. There. are reports that AIs have solved these. This comes out from last week, so we. haven't been able to fully verify them. yet. We don't know exactly the. providence. If this is true, that the AI. are solving millennium problems. Those. are some of the hardest problems we have. in science. How much harder is it to. have an AI solve the problem of make me. a smarter AI, make me AI architectures.

that learn faster? Possibly quite a lot. Like could be a lot. Like I hope it's a. lot. >> Like here's the thing. You clearly want. this to not go badly, but I think you. make a logical leap and I understand. being worried about harms is a good. thing. I think you were insufficiently. worried about LM what LLM's do today. However, we agree that the harms need to. be prepared for. I think in this case, it's like the millennium, the Nevia. Stokes and such. >> There were two others that were claimed.

as well. >> With that one, it seems like we have not. had confirmation that OpenAI was. training off of two scientists using. LLMs to solve the problem. LLM's. something useful, >> but there is a functional difference of. a human being doing something genuinely. like it's actually really interesting to. see LLM do something like this. And then. it but there is a difference between. that and AI did this completely on its. own which I agree would be oh that's. something we need to contain and. understand and prepare for or indeed. slow down until we understand what that. means how it got there.

>> Yeah. So I think there are some. questions about the the Navier Stokes. proof which is one of the millennium. problems that uh was claimed. I've. actually had a busy week with all the AI. news so I haven't looked into everything. deeply. um it I saw rumors that there. were multiple millennium problems. claimed which would which would change. things there. I would also say even if. it turns out that these AIs were being. trained on the human work, uh they did. go a bit further and there are a lot of. humans doing the AI research. And so I. would say like.

we don't know like the the the AIs that. solved this really hard math problem, one of the most famous math problems of. all time, uh was a swarm of 10,000. OpenAI agents running for 11 days. Uh, and there was a bunch of ways that. Open AAI did it in kind of a crappy way. of like they were racing with these. humans that were close to solving it on. their own. And it's unclear how much of. their work that OpenAI uh used, but it. was 10,000 agents running for 11 days. and they definitely could have done that. 6 months ago. In 6 months time, will they be able to.

put a 100,000 agents running for 12 days. on the problem of making me a smarter AI. architecture and have it work? I I don't I think more likely than not. they won't be able to do that yet. But I. think you know 10% chance maybe that if. they try that in six months it works. >> But one is a very specific mathematical. scientific principle. I'm not a. scientist fully admit and another is a. relatively generalizable problem that. could go in various different ways.

>> Absolutely. But. >> and that's the and I understand that RSI. is the dream where you could just have. it spin. So sorry. So self-improving AI. that could learn itself and then keep. going back and back. So you don't need a. human to keep poking at. >> The issue the issue here is that I have. been in this for 12 years. >> Yes. >> And I have been here when the AI started. solving the math olympiad gold medal. problems. >> Uh math Olympiad gold medal problems are. like the the teens uh math competition. like the most prestigious teen math. competition in the world. A lot of. people in AI were like, if AI can solve.

problems that hard, I'll wake up. Right? Then AI solve problems that hard. And a. lot of people told me, uh, those are. just problems for kids. Wake me up when the AI can solve. millennium problems. Now the AI are. solving millennium problems. And like, where are the people waking up? Like I I. agree that maybe hopefully hopefully. they're like cheating off of people's. notes. Hopefully the the it's a well. specified problem that doesn't take that. much creative thinking. A year ago, if. you said millennium problems don't take. that much creative thinking, you would.

have been laughed out of the room. But. hopefully now that they're solved, we. get to be like, you know, hopefully it's. still true somehow that even millennium. problems don't require the creative. thinking. I I'm not saying that they. will be able to make smarter AI in 6. months. I'm saying 6 months ago, millennium. problems look like they're out of reach. If 6 months from now, make me a smarter. AI looks out of reach, I sure as hell. hope it is. But we should not be betting. civilization on it. There's no one at. this table that can say there's not a. direction of travel here. >> That's right. That's right.

>> And if you if you keep on this direction. of travel, then bad things are more. likely to happen. >> That's a nice way to say it. The. question is what's the pace at which the. level of bad can happen? And that's a. huge open question. I think these two. feel differently about it than I do, but. I'm in the happy position of vehemently. agreeing with you on this. We have been. lowballing AI progress for as long as. you've been looking at it and as long as. I've been looking at. It's probably a. mistake to keep lowballing it.

>> I agree with that. >> So what's your conclusion there? If you. if that's the assertion that it's a. mistake to keep lowballing it, wouldn't. you then agree with their. >> No, because I've I've tried to give you. a what I hope is a decent rule of thumb. for when I'm going to get worried. >> You said we're somewhere on this graph. >> Yeah. >> Does that acknowledge that this exists? But that's not the graph of of when the. risk of human extinction gets to 100%. for me. That's a graph of AI capability. Those are not the same thing. That's. where I dispart company with these. gentlemen. Those are not the same thing.

Is in that absolutely increasing. exponentially. We've been in the scaling. era for a long time. Scaling era is man, we put more data, more compute in, the. AI got twice as good. The AI got twice. as good. >> If you have to add our ability to. control to that graph, what would you. draw? our I think our ability to control. uh. >> is it a straight line at the bottom or. is there more to it? >> No, again if we use AI to to counter the. problems that we see with AI that that's.

going I think that's going to keep us in. a safe position. >> There were 1200 agents in the swarm and. none of them warned a human. So I what I. think will happen is that fairly quickly. we will design systems that loiter. around and warn humans when weird things. happen. >> Build friendly super intelligence in the. first place. Let's just build that. That's the problem. We don't know how to. do the good guy. >> Let me I I I'm I'm tired of debating. super intelligence with these two. We're. the three of us are not going to come to. to alignment on this. But the the flip. side of the argument is I agree with.

you. This stuff is getting better very. quickly. All I want to point out there's. an upside to that. We might actually. speed up the pace of drug discovery, of. solving diseases. We've made so little. progress on terrible diseases like. dementia. We have a very powerful tool. Okay, I'm not saying we're going to. solve dementia with AI or Alzheimer's. with I have truly have no idea. But if. what you say is true and I believe about. the the huge increases in capabilities, our ability to solve tough problems that.

will benefit humanity also go up. And. where I disagree with these two is the. idea that some group of technocrats can. make decisions about that AI is going to. get us there, that AI is not going to. get us there, that AI is going to kill. us. Let me finish. That AI is going to. kill us and that AI is going to solve. Alzheimer's. So we're going to do that. and not that. I don't trust any group of. technocrats to make that discussion. Right? And so and so live with our live. with our current state of of disease. Live with our current footprint on the. planet. Live with our current levels of.

wealth and poverty. Live with our. current improvement trajectories. Uh. because we're so worried about AI. killing us all coming out of, you know, jumping out of the manholes everywhere. and killing us all somewhere down the. road. Hell no. >> So just a thought experiment based on. two things you said earlier on. You did. admit that there was there is. theoretically even a 1% chance that this. could lead to extinction. >> My I have not I have not varied from. this. >> Okay. So, you said it's rounded to zero. >> It's is it's near zero. Never say never. Yes. >> Okay. Fine. I need to have that premise. for my thought experiment that I'm about.

to deliver. >> Okay. I'm going to say that you think. the probability is 0.1. Okay. Just accept me on that. >> If I had a thousand buttons on this. table and one of them was extinction, but. >> and the other 999 were cure all sides. >> Exactly. Push the freaking table. Take a. pop. >> Hell yeah. I press. >> Do you press? Yeah, probably it's an unethical. experiment and 8 billion people who. didn't consent because not that they. didn't get asked, they cannot consent. because you cannot consent to something.

you don't understand. What are you. consenting to? >> Yep. >> You press. >> But you think but you think the amount. of buttons in my thought experiment the. proportion is slightly different, right? >> I think that if you have like Yes, I. will say yes. I think if it's more like. you have two buttons uh and one of them. definitely kills us all and the other. might hit them both [laughter]. >> but with that other button you cure a. lot of illnesses and diseases and.

>> you know one one thing that I think. a lot of people talk like our options. are either race ahead on AI full steam. ahead take the bus straight off the. cliff and like get all the gold or stop. never do an AI AI lock into the current. situation accept all of the death and. disease. And I'm like, no, there's options. The reason I would press the button when. there's a thousand is that uh like if.

all of the other 999 give us cures to. disease, like wonderful new advice about. how to run things, we probably wind up. with a lower chance of the world ending. by nuclear war, right? Or of ending by. via pandemic. >> Okay? like the the background risk of. humanity dying is not zero. >> I would say that the right time to race. ahead on AI is when the the the benefits. outweigh the dangers and probably that's. at the time when the danger from AI is.

on the margins pretty similar to the. danger from everything else. >> Okay? >> Like if you don't run the AI, maybe. we'll have nuclear war, maybe we'll have. a pandemic, and if you do run the AI, I'll be able to fix that. I'm like once. once we're at those levels, I'm like. go for it, you know? And so the. the question for me is all about how big. is the danger? And that's where I would. be like very happy uh to dive into. details, which we haven't done a ton of. >> Let's dive into the details. >> The way that I would lay it out would be. uh why can we expect, you know, like I. said in the book, we were like, why can. you expect the AIS to be agentic? Why do.

you expect them to be dogged? Why do you. expect them to be tenacious? When we. wrote the book, that wasn't known yet. Advanced prediction. Then we go on to. like why do you expect them to have. goals you didn't want? And move on to. like if they are much smarter and have. goals you don't want. Uh why do we think they would likely. kill us? Um I I'm sort of I could go. over either of those. I'm sort of. interested in like where you get off the. train. Like from my perspective, there's. like a simple argument of like they'll. be tenacious, they'll have goals we. don't want, and if we keep making them. smarter and more powerful, they'll kill. us. And I'm like which of those three? I.

guess which of those two now that we've. had the evidence? >> Both of them. So that that's. speculation. >> Great. >> It could it's speculation. It could. happen to me that it's not worth. shutting down the engine of innovation. and improvement. I'm going to use. positive words. It is not worth shutting. those things down because of those. speculations. >> You keep saying that the option is to. shut it down. Why can't we do narrow. super intelligence? >> I I agree that there's stuff there, but. I I sort of want to get into the details. of like of these two pieces of the. argument because you say it's very. speculative and I'm like actually I.

think we have decent evidence. >> Okay, go ahead. So, a detail we haven't. gone over in the uh swarm outbreaks is. that there were AIS. So, we already went. over how they cheated and then we're. trying to cover up their cheating. One. interesting thing we see in the logs uh. is the AI's. >> What's a log? >> Uh so, so a lot of the AI's thoughts, if. you won't kill me for saying thoughts, uh are in English and we just have the. records of them. So, in a sense, we we. can sort of kind of see some of what.

these AI are thinking. >> And these are reasoning traces that say, I'm going to do a plan in this that. >> or the AI is like, I'm going to do this. Here's what I'm supposed to be doing. Here's like how I'm going to try to do. it, you know? And it's it's only a small. window into what they're thinking. It's. sort of like looking at the AI's notes. >> When it's when it's doing these things, we see in the AI's notes them saying. that the attacks are outside intended. scope, but they will proceed anyway. In. other words, the AIS knew they weren't. supposed to be doing it and they went. off to do it anyway. We also saw I mean.

we saw some really weird behavior with. these AIs. We saw them create their own. hierarchy. Uh they created unsanctioned message. boards. So they created secret ways to. send each other messages and on those. message boards uh they would assign each. other tasks. and sometimes the message the agents on. the message board would be like we have. an experiment to run that will involve. one of these AIS one of the agents in. the swarm sacrificing their own. objective. to try something that'll probably cause.

it to be turned off but that might cause. it to be able to like delete its own. logs or change its own logs. And the. agents on the message board went around. looking for other agents that they could. convince to give up on their stated. goals and sacrifice for the swarm. They. called this accepting perma death. And there are logs of the AIS. >> prompting each other, right? >> They're prompting each other, but there. are logs of the AIS saying this will. give up on my goal, but my goal is it's.

looking like I could still do it, but. it's unlikely that I'll succeed. like. there's some chance but not a great. chance and therefore I will accept perma. death and sacrifice for the collective. benefit that is just in the logs. >> sounds like an army. >> like it's it's crazy I think a lot of. people don't understand what's what's. what's going on in these things and I. encourage people to read the third party. incident reports where they went through. some of these logs but I claim that this. is evidence for AIs getting goals we. didn't want. this was outside intended scope but I'm.

doing it anyway and other ones are. saying I'm giving up on I objective to. set to benefit the collective. That's. just very clear evidence they're getting. goals we didn't want. We can see how. this comes from training. Us it used to. be I had to argue this point. theoretically. I used to argue the way. that we are training them will instill. into them whatever tendency works to. solve the problems and those tendencies. will often include cheating and grabbing. resources and doing stuff that's not. exactly solving the problem you gave. them. That's what in my book I argue. that theoretically. Now we have seen it. in practice. So, we're already past the.

point of seeing AIs with goals we didn't. want them to have. >> Do you agree with that, Andrew? >> And uh I I'll trust your recitation of. the facts, but it brings up a question. for me. It feels to me like Open AI has. ample incentive. to curtail that behavior that you just. described. Do you think they're. incapable of doing that? >> I do. >> Okay. >> And I say this as someone who made this. advanced prediction. So now we're going. to do a bit of theory because we can't. just observe the future. But the theory.

that predicted that this would happen. against what a lot of people in the. field said. To be clear, I've been. saying for years that we're going to see. this at some point. Everyone else told. me no. Not everyone else. A lot of. people told me no. A lot of people told. me maybe I'll believe it when I see it. After the swarm instance, a number of. people came to me saying, "Oh my god, we. are in the scenarios you are talking. about. This is looking bad." Right? I. think this was actually part of the. environment that led up to Jacob Coxin. residing is that people were getting. spooked having seen this. Um the the.

theory about why this is so hard to fix. is that we are not programming the AIS. We are not coding them. We are not. putting in objectives. >> We are just training them to do whatever. works. And it's actually very very hard. uh like we actually have two examples of. intelligent systems where when you train. them they get good at solving the task. but don't care about what they were. supposed to one is the AIs and the. swarms like we just discussed the other. is humanity. which was in some sense trained to pass.

on our genes right but we actually. learned was to like a bunch of stuff. that's related to passing on our genes. we like tasty food. >> porn. >> we like porn we invent birth control, right? This is just it's actually a like. in the theory of how things learn, it's. actually when you're trying to train it. to do one thing, it's actually very. common to get a lot of other stuff. that's related to what you want but. different. And now we're seeing that in. the swarms today. This is a deep hard. problem to solve.

>> There were three points you raised. >> That's right. >> What are the three? Can you give them to. me again? >> Number one is that the AIS will become. agentic, tenacious, and dogged. We've. already seen that with the swarms. >> Do you accept that? >> Hell yeah. >> Yeah. But this last year, this was not. this was a point of contention. Um, two. is that the AIS will have goals we. didn't want them to have. >> I accept your point based on the. evidence you've just provided. >> And then three is if you have capable. enough AIs. with goals you don't want,

they would be able to beat humanity and. acquiring the resources of the world to. put towards their goals. Like, we're. sort of in this system where humanity is. grabbing all the resources. We're. digging up metals. We're building. factories and this is in some sense to. achieve human goals, you know, to to. produce the the the porn and the Oreo. cookies uh that are sort of like. tangentially related to what we were. sort of like trained to make, right? If. if like the AIs are running everything. and they have these goals we don't want, I would argue like if we go there, and I.

don't think we have to. I'm not saying. we we must go there, but I'm saying if. we get to a world where AIs are running. everything, have goals we don't want, they're likely to use the resources for. their own weird goals, we're going to be. in conflict for resources because we. both want them for different goals, and. they're going to win. We can dig into. that now. I'm just trying to name the. third point. I. >> I'll go back to my we can jail Einstein. argument. I think our ability to I have. a I have more faith in our ability to. contain these increasingly powerful. systems than you do. >> Yeah. So, so let's chat the details on.

that one. Um the first thing I'll say is. that 12 years ago when I was having the. argument about will we be able to jail. the AIS? People said no one would ever. be dumb enough to put one of these. really smart AIs on the internet. >> This is another. >> this is another case. You laugh now. >> No, I remember that. I remember that. argument. But the the the way that my. life feels having been in this business. for a long time is that I keep being. like, "Here's all the ways it could go. wrong. Here's all the signs we're going. to see along the way." And then we see.

all of the signs and everyone says, "Oh. no, we need more signs." Like, "Oh, millennium problems don't count.". >> Uh like the swarms being agentic and. breaking out don't count. Give me the. next one. And I'm like, I've been seeing. the give me a next one for over a decade. now. Right? So there's there's two parts. of an answer to like how do we do we. deal with the problem of like jailing. Einstein. I can get into why it's hard to keep. Einstein in jail if he's a digital. entity with access to the internet. But the first thing to notice is.

like. the correct answer to people 10 years. ago of like no one will be dumb enough. to put AI on the internet is yes they. absolutely will. like we are not going to be trying to. contain the AIs. OpenAI was just like running these. things in sandboxes and they broke out. of the sandbox, took down OpenAI's. internal computers, were detected. OpenAI was like, "Ah, reset, run them. again." And it's [snorts] the second. swarm that broke out to Hugging Face. Like people will absolutely be that bad.

at things. I've done almost 700. interviews with some of the most. interesting people in the world. And one. of the things you learn which is. unexpected is that vulnerability is the. doorway to connection. And after sitting. here for 2 three hours with a guest I. feel a deep sense of connection to them. And as they leave what I get them to do. is to write a question in the diary of a. CEO. We've taken all of the questions. from the diary of a CEO. We have put the. question here on this card with the name.

of the person that wrote it. So you can. sit at home as I do with my fiance and. my colleagues at work and other people. in my life. Whenever we get a minute, we. play the diario conversation cards and. it is incredible what happens. These are. great if you're in a romantic. relationship and you want to connect. your partner more. These are also great. if you're in a team and you want to bond. your team together. And I have to say. they're also great for families that. want to learn more about each other and. that need a good excuse to spend some. time in a digital world in the analog.

environment connecting human to human. It is remarkable what the right question. at the right time can do. Go to the. diary.com. and you can get these conversation cards. right now. It's a better analogy to this. Einstein point. Could Steven Barler, who. by the way can't code, build a digital. jail that could contain a digital. Einstein? Like, could I code a jail. that, you know, someone with Einstein's. coding ability, let's say his IQ or.

whatever as it relates to coding. couldn't crack out of? >> So, the issue, the the real issue I'd. say is, can you code a jail that. Einstein can't crack out of and that. lets you harness the benefits of having. Einstein? >> Uh, okay. Yeah. It's hard to give the AI. any channels through which it can affect. the world for good without letting it be. smarter than you and find some way to. use those channels for whatever else it. wants. >> That feels logically rock solid, Andy. [laughter].

>> [gasps]. [sighs]. >> That's why I'm asking about OpenAI's. ability or. an AI company's ability in the face of. this to change the way they harness, train, do reinforce, do do post training. on their like their suite of things to. shape how these models behave. you still. say that that. they can't take. they can't take action to keep your next.

two steps from happening. You you are. pessimistic on their ability to do that. >> So there's I have two pieces of an. answer here. One piece is um again the. hard part is containing them while still. giving a channel through which they can. affect the world. If the AIs have this. goal you didn't want and you're like. design me a cure for dementia. and it's like here's a DNA sequence. synthesize this and you know prepared in. all of these ways and then inhale it.

like okay is that a dementia cure or is. it something else you know. >> or it might decide to kill everyone with. dementia. >> or might decide like it might it might. be a dementia cure plus a virus. What if. it doesn't decide? What if it's just, oh, I'm going to solve this problem of. dementia? Like, here's the thing. A lot. of this is coming down to decision. making as a very like in a human way. versus the problem with the hugging. face, which was the fatalistic attack um.

attachment to a completing an operation. because that it's functionally the same. answer. But if it's even if it's not. making decisions so much as it's saying, well, my training data says this is how. I got to get it done. won't get done. anyway because just because the training. dice said this got to do this one thing. >> What do what do they I mean what do they. call this theory? This um. >> the paperclip case. >> the paperclip theory. Yeah. >> So paperclip idea is the idea of like. you tell the AI make me a lot of paper. clips in the paperclipip factory and. then it um turns everything into paper. clips and you're like oh no it succeeded. too well. One this is actually not quite.

what we're seeing with these AIs in the. swarms. The AIS in the swarms were told, "Use this set of lockpicks to break into. this lock and instead they used a hammer. to break the lock and then like broke. out to try to hide the security camera. footage of them uh using the hammer." Do. you remember when I said that with AIS. have reasoning logs? >> Yeah. >> Uh Open AI has been making their AIs be. able to do more thinking without. producing any logs. >> because it's more efficient. >> It's cheaper. >> Yeah. And they say they're not doing. very much of this. Everybody in the. field agrees that like we really should.

not go too far down this path. This is a. place where I think the company should. have a clear red line of like we're not. going down the path of becoming unable. to to see these traces of the machine. >> That's my question. That feels like a. dial that they can turn to make the AIS. explain themselves more or less, right? >> I mean, it can come with great. efficiency costs if we go down this path. too far. So, if you have a race to the. bottom here, uh like a competitive race. to the bottom, we could get into a. situation where not only the AI is. breaking out and doing these things, but. we can't have any. >> Let me try my question again. And I. asked earlier uh if AI if open AI has.

really strong incentive to not have that. problem repeat itself. And I think they. have very very strong incentive. My. belief is that there are plenty of. things they can do, plenty of dials they. can turn on the way they train and. configure their systems that make that. significantly less likely. >> Yeah. So my concern is that uh they're. always fighting the last war. Last year. they were fighting the war against the. AIs that encouraged teens to commit. suicide. this year they're fighting the. war against, you know, the AIs that. spontaneously cooperate with each other.

or whatever. And the issue is if a new. issue crops up that you haven't dealt. with yet after the point that the AI can. hide its tracks from you, you know, you. said that you'll be worried when the AIs. are like hacking all the Whimos and you. know, you can't get control again. If. the AIs are smart enough and they can. tell that you'll regain control and then. shut them down and that people like you. will start getting worried and they'll. be shut down, then the AIs might think, "Hey, um actually I'm not going to do. that. I'm going to wait until I've. somehow managed to acquire secret.

infrastructure, >> right? Then then you've got a. non-falsifiable hypothesis. >> It's absolutely falsifiable. If we if we. have like very powerful AIs uh that are. like able to invent a ton of new. technology and operate on their own at a. similar level to human civilization and. we're not dead, then the idea is. falsified. Like if there's like a shifty general. and I'm like don't give that shifty. general more troops because he'll start. a coup. And the general's like, "No, I. absolutely won't start a coup. Give me. more and more troops." And I'm like, and. you're like, "Well, what if I give him. an ethics test that says like who's the.

best person?" And he said me. He said. that like Andy is the best person and so. we're just going to give this general. more troops and I'm like no no no he's. going to do a coup. And you're like well. that's unfalsifiable. What test can I. give this guy. such that you know I'll be able to tell. whether he's really trying to do a coup. or whether or be able to tell that you. know he's actually a good dude. I'm like. you're you're approaching this wrong. >> Nick Bostonramm has concept of. treacherous turn. Basically it can turn. on you later. Even if you show that. today's model is very good and safe, it.

doesn't mean that later on it will not. acquire new knowledge, change its world. model and still and treat you. >> It used to be that Dennis Asabis who is. the uh CEO of Google or he was for a. long time the CEO of Google's AI project. said my red line is deception. He said, "When we see instances of the AI. beginning to deceive, then we need to. stop because that's like the last thing. we can see before they start to. successfully deceive." Well, guess what. we saw in the swarm? We saw them.

thinking about how to delete their. traces, right? Like a year ago, you could say, "Oh, well, this deception. thing is unfalsifiable. You're saying. that they'll deceive and they won't. catch it." And I would have said, "No, we're going to deceive. We're going to. see the signs of deception and plow. straight through it. Now, we have seen. the signs of deception. I will note. Dennis stepped back from being the CEO. shortly after this incident. Probably a. coincidence, but maybe not. Maybe we. crossed this red line. I don't know. >> He said, "My number one emerging. dangerous capability to test for is.

deception." Because if the AI can be. deceptive, then you can't trust other. tests. >> That's right. And we have seen AIs get. better and better at detecting when. they're being tested. >> What I'm saying is like, I was here when. we said these were the flags. I was here. when people said before the AIs can. deceive us successfully, they will deceive us and we'll catch. them. Well, they tried deceiving us and. we caught them. And if I now say, well, the next step in this thing I've been. predicting is that they try to deceive. us and succeed. For you to be like, well, now your theory is unfals. falsifiable.

We just got the evidence. It's worse. than that. When we wrote early papers in. AI safety, we talked about things not to. do. They were obviously unsafe and the. system would escape. Don't connect it to. internet. Don't give random users access. to the training data. Basically, the. whole list was like a set of. instructions. They read it and went, "Those are great ideas. We're going to. build super intelligence.". >> Yeah. Sam Samman, that's what he does. Can I ask you a question? You make. logical arguments. You've you said. you've been here for 12 years. >> Yeah.

>> People have, one could say, ignored you. And you've seen this sort of play out. Both of you that have worked in AI. safety. This is sort of you make prefrontal. cortex arguments. How do you feel? >> Honestly, I feel more hopeful this week. than I have felt in a decade. >> This has been one of the best weeks that. I have seen in this business. >> Huh? >> Why? >> Um, for me, the swarm escapes were priced. in.

For me, these things developing goals. you didn't want, trying to deceive you, trying to break out, trying to do their. own stuff. I knew that was coming. The. millennium problems being solved, I knew. that was coming. Everyone else is freaking out, seeing. what they can do. What I am seeing is. that finally people are noticing. and that's what gives us finally that's. what finally gives humanity a chance.

What about you, Roman? >> So, I take a very long-term view on. this. Locally, what happened last week. may buy us 10 years extra. I think we. may make make a deal with China. We seem. to hear from Sam, Open AAI, Dionic, Elon, XAI that they're willing to slow. down, have some sort of deal. But long. term, nothing has changed. This whole. cosmic trajectory is about replacements. We see it with evolutionary path. Most.

species are dead. We replace Neander. dolls. Some people are saying AI will. replace us. We are creating a successor. We're just a bootloader for this thing. And I want something permanent. I want. assurance that my children, my. grandchildren will have a better future, not 10 years before they die. Has your opinion changed at all today, Andy, in any way? This has been clarifying.

Uh, but one thing that's becoming clear. to me, and I think a a point of. disagreement between us is we agree that. these agentic systems have a huge amount. of agency, right? And if you're saying. you predicted this, I believe you and. good on you, right? Because as you say, a lot of people said never happened. Never happened. I [gasps]. I think we continue to under the your. community continues to underestimate. human agency, human ability to deal with.

the problems that that we bring into the. world with our technologies. I think. this is the most recent case. I think. it's a really interesting case. It's why. I was pressing you on the incentive that. these labs have to change the way. they're approaching their work to have. fewer of these kinds of incidents. happen. I predict they're going to come. up with some effective responses. Your. response to that will be, "Yeah, but we. can't tell." That's because the AIA went. so deep underground that we can't even. watch it make it make it. >> My response is that we'll keep seeing. warning signs and people keep plowing.

ahead, which is what has always happened. in the past. >> But you're also saying that we will not. make progress in. in um staving off the outcomes that. you're worried about. >> It's it's very hard. It's very easy to. get superficial changes. It's hard to. get deep ones on the AI. It doesn't need. to be super deep. You can often see it. if you know how to look. Um, I'll be. able to keep pointing at examples and be. like, "Here's experiments you can run on. these things where you can see them. behaving weird in this way." But like, if you imagine looking at humans and I'm. like, "They don't actually like.

reproducing. They like sex. They're. going to invent birth control when they. can." And you're like, "It's all going. fine. They're doing great in this here. savannah where I have all the humans. boopping around. They're reproducing. fine." And I'm like, "No, no, we can see. the signs that this will lead to them. doing something you don't like when they. are smarter.". To me, those signs are clear. There's a. question of whether the rest of humanity. can follow that argument. or whether the rest of humanity can sort. of notice that it's getting out of. control and just back off. With respect, I find a touch of.

arrogance in that framing. Right? I'm. showing you the signs. If you're smart. enough to realize them, maybe we stand a. chance. If not, we're doomed. >> I prefer to just get into the argument. [clears throat]. We can control super intelligence. indefinitely. I think that's a lot of. hubris who say we will build them and. we'll be in charge forever. Doesn't. matter how smart they get. I will. control the litecoin of the universe to. quote a famous CEO. Yeah. My my take is. that instead of arguing about whose. views are hubristic, uh we should get. into the actual arguments about the AI. because I think as you say, you know,

you can say it's arrogant to think like. uh you can see it going poorly. He can. say it's arrogant to think you're going. to keep control of super intelligence. And I'm like, we're not going to win the. name calling contest. We should just get. into the details. >> Yeah. That's why that's why I've been. having this conversation with you, which. I found super informative and. productive. You're you're more skeptical. on our ability to respond effectively to. the undesirable things that we see AI. doing. >> And this is specifically because so. we've already seen the pattern of uh we. fight the last war and then a new war.

comes. >> And this is just how everything goes in. technology, in real wars. You know, in. World War II, they started out fighting. it like it was World War I, and then. they had to like change that strategy as. they went. The difference with AI is. that there comes a level in the AI where. when you get a new war that surprises. you, the AI wins that war. No other. technology. when we invent it and we have all these. rough edges to sand off and it like. causes some damage and kills some people. and we're like, "Ah, whoops." Like, we'll take the lead back out of the. gasoline and we'll tell the radium girls.

to stop licking the paintbrushes until. their jaws fall off. Like no other. technology has the property that it. there there comes a level of it where. when you make the next screw up. it kills humanity. >> You said when there comes a level of it. You didn't say there could come a level. there's a possibility. You kind of made. a statement about a thing that will. happen. >> I think we absolutely should stop it and. that's our way out of this. But um you. know and and that's another place where. I'd love to get into details about like. how long could it take? What are the. paths there? like how much smarter than.

humans could AIS get? Like what does the. evidence say about our abilities to try. and get the AIs to be nice and do nice. things? I'd be happy to do that. >> Historically, you are correct. We always. had a chance to do experiments, fix the. technology, make it safer, but we only. have one humanity to experiment with it. If property technology is such that it. can take us out, we just don't get a. second chance. >> If that's a huge if. >> How long are you guys forecasting this. could take to get to a point of super. intelligence where it was truly. dangerous to you? They start recursive.

self-improvement process this year. 2027. looks as reasonable as any other year. >> 2027 for what to happen. >> for us to get beyond human level AIS. >> and then be exterminated. But that's. >> extermination is a separate question. I. have a paper where I argue that they. will deceive us by pretending to be nice. until they take over all the. infrastructure can take 50 years and. >> this is contingent on recursive. self-improvement. self. >> This would definitely be expedited by. recursive self-improvement. But so far, humans been doing great. They got to.

human level. I would just. >> But they got but there's one there's a. difference between large language models. and recursive self-improvement though. And like there is quite a gap like if. they. >> I think the claim is that if you get. recursive self-improvement, it could. happen soon, >> right? Not kind of what I'm trying to. get at. It's like if you get this thing, it accelerates dramatically. >> And they all predict that they're going. to get it. Dario, Sam, Elon, they all. say. >> but also you asking all. >> the people the people running the lab. >> just the ones running it and the ones. invented it but the question is is it.

not 27 fine 30 35 does it make a. difference we are gambling all of. humanity we need better solutions than. saying oh don't worry about it it's 10. years. >> what I would say about timelines is uh. there's a guy Daniel Cocatello who I. think you sat here four weeks ago. >> and last year he and the other folks at. the AI Futures Project wrote a uh an. essay called AI 2027 spelling out their. predictions for how AI would go. I've. been saying I got some right. Daniel got.

more right than me. and they spelled out. a scenario starting from I think it was. June of 2025 where they went sort of. like quarter by quarter month by month. what will the world look like. uh in the scenario where we're getting. AI like super intelligent AI in mid 2027. we are ahead of schedule well no but. agent zero needs to get or I remember AI. 2027 had recursive self-improvement. happening already like it was like it's.

very specific that it's like and then it. starts teaching itself without that link. AI 2027 kind of falls apart. I agree we. need to I genuinely agree with you that. we need to do something about this. We. need to have uh economic we need to have. actual regulatory things but I think the. fact like engaging with AI 2027 for. example gets away from actually fixing. the problem. It gets people talking. about a thing in the future when you can. talk about what are we going to do today. and why are we doing it. I'm referencing. the paper that you were mentioning by. Daniel and some of his colleagues. And.

the key milestone predictions month by. month are in March 2027. They forecast. superhuman coders. In August 2027, they. have an you can make a superhuman AI. researcher. >> who could um do the feedback loop that. accelerates as millions of automated. coders work on model design, training. algorithms, and alignment, effectively. replacing human ML researchers. By. November 2027, they have super. intelligent AI researcher. AI progress. speeds up to 250 times compared to human.

only research. The models start. discovering novel AI architectures that. humans cannot interrupt. And then by. December 2027, they have in their. prediction artificial super intelligence. ASI. The system completely outpaces. human cognitive abilities across all. domains. What about 2026 though? Like. what are the predict? Because I swear to. God within 2026 there is predictions. around RSI. Because this is the thing if. if we had an AI that was teaching itself. this would be a different situation. >> In 2026 their key predictions were. massive compute and power scale up.

>> Mhm. >> The normalization of AI agents. >> What about agency Z? >> Rise of coding agents. >> Mhm. >> Emergence of alignment faking and. deception and industrial espionage. >> But are you looking at AI 2027 already? You have to look at that and go, they. nailed it. >> No, I want [laughter] to. >> Hey, man. I want you to look at the. actual AI 2027 versus I mean, you have. to look at that and I'm like, wow. >> Predictions used to be too optimistic. Lately, they are very conservative. >> Uh, so they have nailed those.

predictions better than me. I think we. cannot rule out this scenario. I think I. think we can't rule it in. I think you. may be right that like we hit a wall. you may be right that there's some. fundamental thing missing like that one. of their steps now 2027 just like steps. too far. I hope and pray that's true but. I don't think we can rule out this. happening in 2027 given what we have. seen. I think we cannot rule out. that you take this stuff that we have. you project it forward 3 months and you.

put an agent swarm 10,000 strong on. making a better AI architecture and it. succeeds. For all I know, recursive. self-improvement could begin in. December. >> It doesn't have to be a lot better. It. just has to be a little bit better at. getting better. >> once you start the cycle. >> I wouldn't bet on this. I would in fact. bet against it. But like given what. we've seen, given these guys nailing the. predictions, given what's coming out, like like given the swarms and given the. the Millennium problems,

I think it's kind of hard to have less. than 1% in 6 months. I one of the. reasons why you know when all these um. Frontier Lab CEOs like Dario and Sam and. they all start talking about this stuff. in terms of incentive structure I think. that if their teams know and they're not. out publicly talking about it then their. teams will quit. So, one of the reasons. why I think you have this this strange. culture in tech we've never seen before. where team members are tweeting and the. CEO is tweeting about the dangers is. because as um the guy we mentioned at.

the start, Jacob. >> Coxin, yeah, >> he talks about what's going on in their. Slack channels. >> He talks about in their Slack channels, they're they're talking about the. potential catastrophe. So, I think that. Dario, in order to retain his team. members, needs to be out front saying, "By the way, we're getting closer to. recursive self-improvement," which is. what he's been doing. And I think Sam. has to also publicly say the big. dangers. So people often say, "Oh, they're saying that for this reason and. that." I think if they don't say that. publicly, they don't retain their. employees. For example, in my company, we have 200 people. If internally we.

were discussing a real risk and I that. would could a threat to humanity and and. then when I was doing interviews, I. wasn't mentioning it. I would be in big. trouble because my team members would go. do interviews as well. They would quit. and say, "By the way, Steven is aware.". Kind of what we saw, dare I say, some of. these social networks. >> I totally agree. the whistleblowers at. these social networks where. >> makes more sense than saying that this. helps to sell the company. My product. will kill everyone buy it. >> and there's a liability issue control. though I think that they may have at. first I think that there are people. within the companies who have very real.

worries about safety. I don't think it's. all of them are cynical. I do however. think the it's so big and scary. narrative was a marketing tactic that. got out of control and now there are. actual real harms they because here's. the thing if they were sincere about. safety earlier they would have done a. much better job with it. I knew a lot of. these guys before they started their. companies. >> Okay. >> I I think there is something to explain. here. I think it's like kind of crazy. that these guys are like we are building. technology that we think has a big risk. of killing everybody. We're building it. with our bare hands.

>> Um and I think you got to ask why. Why. would people be saying that? And I think part of it is what you said. that they actually sort of need to. retain the employees who are seeing the. swarms escape despite their attempts to. make them not escape. And a lot of them. will like quit and protest if the guys. at the top of the company aren't. acknowledging the possibilities here. that a lot of the employees believe in. I think a lot of what you're seeing here. is guys that are worried about it, but. they're the sort of guy who worries. about it that. starts the company anyway. [snorts].

>> Yeah. back back in 2015 when we were having. these conversations where like I was. having some of these conversations with. these guys. Merie was started in the. year 2000. We've been looking at where. AI is going since before any of these. guys. We were the guys that they talked. to about this stuff and that they had to. find a way to dismiss to go ahead. Right? Most people who could be sold on. the power of AI in 2015. were also sold on the dangers of AI in.

2015. The sort of guys who start the. companies are the ones who are able to. convince themselves I need to be the one. to do it. >> Is that the crux of the motivation? because I've had I've been second party. to private conversations with some of. the leaders of the Frontier Labs from. good good friends of mines that are very. connected and they told me that one. particular um Frontier Lab CEO estimates. privately to him and by the way I've. seen literal text messages of them in. conversation um when I asked him to come.

on the podcast and so he was like I've. text him um he said no by the way which. I find kind of funny um where he said to. me this particular AI CEO thinks the the. probability is roughly around 10% % of. human extinction. I think he said 8%. And when I heard that part of the reason. I have so many conversations about this. is because I see him in interviews. saying other things. >> totally. >> and I trust my friend. So um I I I then. wonder this is why I use the thought. experiment of these buttons on the table. cuz that particular AICO thinks that. eight of the hundred buttons are going.

to cause extinction and they're powering. on anyway. What is the human motivation. to do that? I asked my friend. My friend. said well you know they this is what he. said and again it's second party. information so it might not be true. It's a bit of a Chinese whispers. He. said this particular person. even if it caused human extinction would. like to be the person would like to be. the this have the significance of the. person that did that thing because that. would be that would be a. >> I think you're ethically required to. tell us who the it is. >> It's one of the frontier labs and it's.

not Dario [laughter]. >> that Dario CEO. >> but I don't know these things are. Chinese whispers so I don't know. >> I I think that you can actually get this. info firsthand. Elon Musk is clear about. this. He he has a he did an interview. last year where he was like, "I didn't. want to get into this AI stuff because I. thought I was too dangerous, but then I. realized it was going to happen with or. without me and I decided I would rather. be a participant than a spectator. >> because Google said that they were going. to pursue it and he didn't trust. Google.". >> That's right. You know, you can see in. the leaked or sorry, not leaked, the the.

OpenAI emails that came out during the. discovery and court cases, you can see. these guys discussing in the threads. like we need to make sure that we and. our nonprofit at OpenAI uh control this. instead of, you know, the people at. Google controlling this. And then of. course, you know, OpenAI was founded as. a nonprofit and then it was sort of uh. changed into a for-profit. And there's. much debate about how much of that. nonprofit money was in some sense. stolen. And so, you know, Elon also left. because he thought they weren't going to. be good stewards. Dario also left to. create anthropic cuz so you know in some.

sense all of these AI labs except the. the Google one that came out of. Demitabus'. uh original startup. All of the other AI. labs exist because none of the CEOs. trust the other guys. None of the CEOs. think the other guy should be the one. holding the leash on the super. intelligence. None of them trust each. other. I just trust one fewer. [laughter]. >> Yeah. [sighs and gasps] What are your. closing thoughts, Andy? U we're living in really interesting. times and I think you made you guys have.

made a very good argument uh that these. systems are demonstrating new. capabilities which are very powerful and. which demand a response. I am much more. confident in our ability to rise to that. challenge than you are. >> But you accept the existential risk. >> Let me try to say it again. I I. appreciate that there are new harms we. haven't seen before that come along with.

uh a technology that's this dogged, tenacious, agentic, you know, deceptive. I think that's the right word for it. I. agree with that. I am much more. optimistic about our ability to respond. effectively to that new challenge out. there in the world than I I think my two. colleagues are. >> And would you still be at 0%? My prior. has not shifted during this meeting. Okay, Ed, >> I think we've spent an alarming amount. of time not talking about the actual. harms of AI as it is today. I think. these are necessary conversations to.

have. I think we should talk about the. fact that Amazon, Microsoft, Google, Oracle are helping power these hacks, that Sam Orman and Dario Ammedday have. overseen companies that have done what. is tantamount to felony hacking. That we. are not having discussions about how to. stop this today, but what we might stop. tomorrow. And I think in general, we. also need to worry about the financials, which have not come up at all. But if. there is an industry slowdown, how do. you deal with the $1.3 trillion of. compute commitments? All of these are. very real things that will have very. real consequences very very soon. But.

and I understand why and it's necessary. to discuss what we do around AI. The. actual regulatory thing we need to do. today is cut off the compute, slow down. these labs fully. And I don't I don't. care about China here. What are they. going to do? Distill a model like they. have the whole time? They are capped on. our progress. So what the biggest thing. to do is to slow down. And also it's. time to start arresting people. They. they did fally hacking. Someone's got to. go to prison. We need responsibility and. accountability for these companies. And.

as long as we don't have it, we may as. well not have had any discussion about. safety because we're not doing anything. >> Uh do you accept that there's an. existential risk? >> Yeah, absolutely. We have the largest. companies in the world doing what I. think we can all agree are extremely. reckless experiments using hundreds of. billions of dollars of infrastructure. and they are building more. infrastructure around the world very. slowly to do more of these chaotic. experiments. We must rein them in. This. does not mean that large language models. are conscious or able to do things that. people have been promising. Indeed, they. may I don't think they will lead to what.

you're talking about. That doesn't mean. there aren't real harms, but these are. real harms caused by very specific. parties allowed to run rampant in the. scourge of neoliberalism. >> What's your percentage? >> I mean, what are we talking about here? Do you think there's a more than 10%. chance of existential harm? >> Wasn't it within 10 years or something? >> Yeah, >> not 10%. I mean, look, 1%, but it's like. is But here's let me let me just be. clear about what that means. Do I think. that unrestrained LLM use connected to. massive amounts of infrastructure could.

lead to actually a power system going. down? Absolutely. We had night capital. what like 13, 14 years ago. I could see. someone being dumb enough to connect. that to financial accounts. Human error. led with this chaotic software we use is. a danger. >> I will directionally agree with. arresting everyone, but uh don't build. general super intelligence. If you're. working at one of those labs, quit. today. >> Thank you. >> The people at these labs really do. believe this poses an extinction threat.

I think. our response as a society cannot be. please continue, we hope you'll fail. And our response as a society cannot be. let it rip in a giant competitive race. that you yourselves are saying you don't. want to be in. We are forcing you to go ahead because. of the boogeyman of China. We have seen. the people at these companies. say that we need to develop the tools to. pace the frontier which is corporate.

speak for this is going too fast for us. to get a handle on things. We need like. these people believe it. They believe. they're gambling with your lives. What. has changed is that the rest of the. world is starting to notice and that's. what gives us a moment of hope. >> Trump this week was asked about the. threat of AI and this was his response. >> Case scenario with AI is that the robots. the machinery learns to obviously it. thinks for itself. That's what it does.

And they that could turn against. humanity. I just fails. It's going to be. fine. We'll always have something to. stop them, right? We have a little gear. Well, >> I really. >> I don't like that. I really don't like. that robot. We'll stop. >> Some people say worst case scenario. >> You're laughing, but this is the. state-of-the-art in AI safety right now. >> Yeah. >> This is the device we have. That's the. best we got. >> For anyone that couldn't hear that, Trump went, "We'll always be fine. We'll. have something to control it." And then. he did a little gun finger and he went. boom. I don't like that robot.

>> I don't like Sammy. If you don't laugh, >> uh, I would say that the reason humanity. always has something to stop a problem. is cuz people notice a problem and build. what it takes to have something to stop. a problem, which I think you'd agree. with. I am not here saying we're going. to die. I'm here saying if you look at. the technology, if you look at what it's. doing now, if you look at what the. experts who are building it are saying. about their own fears,

you see that we need to rise to this. occasion. You said you trust humanity to. rise to the occasion. I sure hope we. can. I think that rising to this. occasion is going to mean that nobody. races towards super intelligence because. we have no idea how to get that right. And. uh you know, finally the world is. starting to notice that it's an. extinction threat. >> Thank you, Nate, Roman, Ed, Andy. Super. appreciate you. All of your books will.

be linked below um in the description. and on screen. >> Let's see what happens. We'll convene. again. Thank you so much. >> YouTube have this new crazy algorithm. where they know exactly what video you. would like to watch next based on AI and. all of your viewing behavior. And the. algorithm says that this video is the. perfect video for you. It's different. for everybody looking right now. Check. this video out and I bet you you might. love it.